Index: doc/html/libEF_8h-source.html
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 <a name="l00125"></a>00125                         <a class="code" href="classbdm_1_1enorm.html" title="Gaussian density with positive definite (decomposed) covariance matrix.">enorm</a> ( <span class="keyword">const</span> vec &amp;<a class="code" href="classbdm_1_1enorm.html#c702a194720853570d08b65482f842c7" title="mean value">mu</a>,<span class="keyword">const</span> sq_T &amp;<a class="code" href="classbdm_1_1enorm.html#2d92dde696b2a7a5b10ddef5d22ba2c2" title="Covariance matrix in decomposed form.">R</a> ) {set_parameters ( mu,R );}
 <a name="l00126"></a>00126                         <span class="keywordtype">void</span> set_parameters ( <span class="keyword">const</span> vec &amp;<a class="code" href="classbdm_1_1enorm.html#c702a194720853570d08b65482f842c7" title="mean value">mu</a>,<span class="keyword">const</span> sq_T &amp;<a class="code" href="classbdm_1_1enorm.html#2d92dde696b2a7a5b10ddef5d22ba2c2" title="Covariance matrix in decomposed form.">R</a> );
-<a name="l00128"></a>00128 
-<a name="l00131"></a>00131 
-<a name="l00133"></a>00133                         <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1enorm.html#d2e0d3a1e30ab3ab04df2d0c43ae74a2" title="dupdate in exponential form (not really handy)">dupdate</a> ( mat &amp;v,<span class="keywordtype">double</span> nu=1.0 );
-<a name="l00134"></a>00134 
-<a name="l00135"></a>00135                         vec <a class="code" href="classbdm_1_1enorm.html#e1a48f52351ec3a349bd443b713b1766" title="Returns a sample,  from density .">sample</a>() <span class="keyword">const</span>;
-<a name="l00136"></a>00136                         mat <a class="code" href="classbdm_1_1enorm.html#e1a48f52351ec3a349bd443b713b1766" title="Returns a sample,  from density .">sample</a> ( <span class="keywordtype">int</span> N ) <span class="keyword">const</span>;
-<a name="l00137"></a>00137                         <span class="keywordtype">double</span> <a class="code" href="classbdm_1_1enorm.html#e13aeed5b543b2179bacdc4fa2ae47a3" title="Evaluate normalized log-probability.">evallog_nn</a> ( <span class="keyword">const</span> vec &amp;val ) <span class="keyword">const</span>;
-<a name="l00138"></a>00138                         <span class="keywordtype">double</span> <a class="code" href="classbdm_1_1enorm.html#25785343aff102cc5df1cab08ba16d32" title="logarithm of the normalizing constant, ">lognc</a> () <span class="keyword">const</span>;
-<a name="l00139"></a><a class="code" href="classbdm_1_1enorm.html#b2fa2915c35366392fe9bb022ca1a600">00139</a>                         vec <a class="code" href="classbdm_1_1enorm.html#b2fa2915c35366392fe9bb022ca1a600" title="return expected value">mean</a>()<span class="keyword"> const </span>{<span class="keywordflow">return</span> <a class="code" href="classbdm_1_1enorm.html#c702a194720853570d08b65482f842c7" title="mean value">mu</a>;}
-<a name="l00140"></a><a class="code" href="classbdm_1_1enorm.html#729c75ef0fa8abae03d58ad1f81e6773">00140</a>                         vec <a class="code" href="classbdm_1_1enorm.html#729c75ef0fa8abae03d58ad1f81e6773" title="return expected variance (not covariance!)">variance</a>()<span class="keyword"> const </span>{<span class="keywordflow">return</span> diag ( <a class="code" href="classbdm_1_1enorm.html#2d92dde696b2a7a5b10ddef5d22ba2c2" title="Covariance matrix in decomposed form.">R</a>.to_mat() );}
-<a name="l00141"></a>00141 <span class="comment">//      mlnorm&lt;sq_T&gt;* condition ( const RV &amp;rvn ) const ; &lt;=========== fails to cmpile. Why?</span>
-<a name="l00142"></a>00142                         <a class="code" href="classbdm_1_1mpdf.html" title="Conditional probability density, e.g. modeling some dependencies.">mpdf</a>* <a class="code" href="classbdm_1_1enorm.html#baea4d49c657342b58297d68cda16d26" title="Return conditional density on the given RV, the remaining rvs will be in conditioning...">condition</a> ( <span class="keyword">const</span> <a class="code" href="classbdm_1_1RV.html" title="Class representing variables, most often random variables.">RV</a> &amp;rvn ) <span class="keyword">const</span> ;
-<a name="l00143"></a>00143         <a class="code" href="classbdm_1_1enorm.html" title="Gaussian density with positive definite (decomposed) covariance matrix.">enorm&lt;sq_T&gt;</a>* <a class="code" href="classbdm_1_1enorm.html#c2996bdaffad38fbe0fc776db54c9d80" title="Return marginal density on the given RV, the remainig rvs are intergrated out.">marginal</a> ( <span class="keyword">const</span> <a class="code" href="classbdm_1_1RV.html" title="Class representing variables, most often random variables.">RV</a> &amp;<a class="code" href="classbdm_1_1epdf.html#62c5b8ff71d9ebe6cd58d3c342eb1dc8" title="Description of the random variable.">rv</a> ) <span class="keyword">const</span>;
-<a name="l00144"></a>00144 <span class="comment">//                      epdf* marginal ( const RV &amp;rv ) const;</span>
-<a name="l00146"></a>00146 <span class="comment"></span>
-<a name="l00149"></a>00149 
-<a name="l00150"></a>00150                         vec&amp; _mu() {<span class="keywordflow">return</span> <a class="code" href="classbdm_1_1enorm.html#c702a194720853570d08b65482f842c7" title="mean value">mu</a>;}
-<a name="l00151"></a>00151                         <span class="keywordtype">void</span> set_mu ( <span class="keyword">const</span> vec mu0 ) { <a class="code" href="classbdm_1_1enorm.html#c702a194720853570d08b65482f842c7" title="mean value">mu</a>=mu0;}
-<a name="l00152"></a>00152                         sq_T&amp; _R() {<span class="keywordflow">return</span> <a class="code" href="classbdm_1_1enorm.html#2d92dde696b2a7a5b10ddef5d22ba2c2" title="Covariance matrix in decomposed form.">R</a>;}
-<a name="l00153"></a>00153                         <span class="keyword">const</span> sq_T&amp; _R()<span class="keyword"> const </span>{<span class="keywordflow">return</span> <a class="code" href="classbdm_1_1enorm.html#2d92dde696b2a7a5b10ddef5d22ba2c2" title="Covariance matrix in decomposed form.">R</a>;}
-<a name="l00155"></a>00155 
-<a name="l00156"></a>00156         };
-<a name="l00157"></a>00157 
-<a name="l00164"></a><a class="code" href="classbdm_1_1egiw.html">00164</a>         <span class="keyword">class </span><a class="code" href="classbdm_1_1egiw.html" title="Gauss-inverse-Wishart density stored in LD form.">egiw</a> : <span class="keyword">public</span> <a class="code" href="classbdm_1_1eEF.html" title="General conjugate exponential family posterior density.">eEF</a>
-<a name="l00165"></a>00165         {
-<a name="l00166"></a>00166                 <span class="keyword">protected</span>:
-<a name="l00168"></a><a class="code" href="classbdm_1_1egiw.html#ae56852845c6af176fd9017dbebbbd52">00168</a>                         <a class="code" href="classldmat.html" title="Matrix stored in LD form, (commonly known as UD).">ldmat</a> <a class="code" href="classbdm_1_1egiw.html#ae56852845c6af176fd9017dbebbbd52" title="Extended information matrix of sufficient statistics.">V</a>;
-<a name="l00170"></a><a class="code" href="classbdm_1_1egiw.html#447eacf19d4f4083872686f044814dc4">00170</a>                         <span class="keywordtype">double</span> <a class="code" href="classbdm_1_1egiw.html#447eacf19d4f4083872686f044814dc4" title="Number of data records (degrees of freedom) of sufficient statistics.">nu</a>;
-<a name="l00172"></a><a class="code" href="classbdm_1_1egiw.html#23e4d78bea7e98840f3da30e76a2b57a">00172</a>                         <span class="keywordtype">int</span> <a class="code" href="classbdm_1_1egiw.html#23e4d78bea7e98840f3da30e76a2b57a" title="Dimension of the output.">dimx</a>;
-<a name="l00174"></a><a class="code" href="classbdm_1_1egiw.html#322414c32d9a21a006a5aab0311f64fd">00174</a>                         <span class="keywordtype">int</span> <a class="code" href="classbdm_1_1egiw.html#322414c32d9a21a006a5aab0311f64fd" title="Dimension of the regressor.">nPsi</a>;
-<a name="l00175"></a>00175                 <span class="keyword">public</span>:
-<a name="l00178"></a>00178                         <a class="code" href="classbdm_1_1egiw.html" title="Gauss-inverse-Wishart density stored in LD form.">egiw</a>() :<a class="code" href="classbdm_1_1eEF.html" title="General conjugate exponential family posterior density.">eEF</a>() {};
-<a name="l00179"></a>00179                         <a class="code" href="classbdm_1_1egiw.html" title="Gauss-inverse-Wishart density stored in LD form.">egiw</a> ( <span class="keywordtype">int</span> dimx0, <a class="code" href="classldmat.html" title="Matrix stored in LD form, (commonly known as UD).">ldmat</a> V0, <span class="keywordtype">double</span> nu0=-1.0 ) :<a class="code" href="classbdm_1_1eEF.html" title="General conjugate exponential family posterior density.">eEF</a>() {set_parameters ( dimx0,V0, nu0 );};
-<a name="l00180"></a>00180 
-<a name="l00181"></a>00181                         <span class="keywordtype">void</span> set_parameters ( <span class="keywordtype">int</span> dimx0, <a class="code" href="classldmat.html" title="Matrix stored in LD form, (commonly known as UD).">ldmat</a> V0, <span class="keywordtype">double</span> nu0=-1.0 )
-<a name="l00182"></a>00182                         {
-<a name="l00183"></a>00183                                 <a class="code" href="classbdm_1_1egiw.html#23e4d78bea7e98840f3da30e76a2b57a" title="Dimension of the output.">dimx</a>=dimx0;
-<a name="l00184"></a>00184                                 <a class="code" href="classbdm_1_1egiw.html#322414c32d9a21a006a5aab0311f64fd" title="Dimension of the regressor.">nPsi</a> = V0.<a class="code" href="group__math.html#g96dfb21865db4f5bd36fa70f9b0b1163" title="access function">rows</a>()-<a class="code" href="classbdm_1_1egiw.html#23e4d78bea7e98840f3da30e76a2b57a" title="Dimension of the output.">dimx</a>;
-<a name="l00185"></a>00185                                 <a class="code" href="classbdm_1_1epdf.html#16adac20ec7fe07e1ea0b27d917788ce" title="dimension of the random variable">dim</a> = <a class="code" href="classbdm_1_1egiw.html#23e4d78bea7e98840f3da30e76a2b57a" title="Dimension of the output.">dimx</a>* ( <a class="code" href="classbdm_1_1egiw.html#23e4d78bea7e98840f3da30e76a2b57a" title="Dimension of the output.">dimx</a>+<a class="code" href="classbdm_1_1egiw.html#322414c32d9a21a006a5aab0311f64fd" title="Dimension of the regressor.">nPsi</a> ); <span class="comment">// size(R) + size(Theta)</span>
-<a name="l00186"></a>00186 
-<a name="l00187"></a>00187                                 <a class="code" href="classbdm_1_1egiw.html#ae56852845c6af176fd9017dbebbbd52" title="Extended information matrix of sufficient statistics.">V</a>=V0;
-<a name="l00188"></a>00188                                 <span class="keywordflow">if</span> ( nu0&lt;0 )
-<a name="l00189"></a>00189                                 {
-<a name="l00190"></a>00190                                         <a class="code" href="classbdm_1_1egiw.html#447eacf19d4f4083872686f044814dc4" title="Number of data records (degrees of freedom) of sufficient statistics.">nu</a> = 0.1 +<a class="code" href="classbdm_1_1egiw.html#322414c32d9a21a006a5aab0311f64fd" title="Dimension of the regressor.">nPsi</a> +2*<a class="code" href="classbdm_1_1egiw.html#23e4d78bea7e98840f3da30e76a2b57a" title="Dimension of the output.">dimx</a> +2; <span class="comment">// +2 assures finite expected value of R</span>
-<a name="l00191"></a>00191                                         <span class="comment">// terms before that are sufficient for finite normalization</span>
-<a name="l00192"></a>00192                                 }
-<a name="l00193"></a>00193                                 <span class="keywordflow">else</span>
-<a name="l00194"></a>00194                                 {
-<a name="l00195"></a>00195                                         <a class="code" href="classbdm_1_1egiw.html#447eacf19d4f4083872686f044814dc4" title="Number of data records (degrees of freedom) of sufficient statistics.">nu</a>=nu0;
-<a name="l00196"></a>00196                                 }
-<a name="l00197"></a>00197                         }
-<a name="l00199"></a>00199 
-<a name="l00200"></a>00200                         vec <a class="code" href="classbdm_1_1egiw.html#920f21548b7a3723923dd108fe514c61" title="Returns a sample,  from density .">sample</a>() <span class="keyword">const</span>;
-<a name="l00201"></a>00201                         vec <a class="code" href="classbdm_1_1egiw.html#df70c05f918c3a6f86d60f10c1fd6ba2" title="return expected value">mean</a>() <span class="keyword">const</span>;
-<a name="l00202"></a>00202                         vec <a class="code" href="classbdm_1_1egiw.html#c1ecc406613cc2341225dc10c3d3b46a" title="return expected variance (not covariance!)">variance</a>() <span class="keyword">const</span>;
-<a name="l00203"></a>00203 
-<a name="l00205"></a>00205                         vec <a class="code" href="classbdm_1_1egiw.html#66d2ba9295c306012b309efcc9e516f0" title="LS estimate of .">est_theta</a>() <span class="keyword">const</span>;
-<a name="l00206"></a>00206 
-<a name="l00208"></a>00208                         <a class="code" href="classldmat.html" title="Matrix stored in LD form, (commonly known as UD).">ldmat</a> <a class="code" href="classbdm_1_1egiw.html#88c321a2051d1afdbb31a098896a717b" title="Covariance of the LS estimate.">est_theta_cov</a>() <span class="keyword">const</span>;
-<a name="l00209"></a>00209 
-<a name="l00210"></a>00210                         <span class="keywordtype">void</span> mean_mat ( mat &amp;M, mat&amp;R ) <span class="keyword">const</span>;
-<a name="l00212"></a>00212                         <span class="keywordtype">double</span> <a class="code" href="classbdm_1_1egiw.html#bfb8e7c619b34ad804a73bff71742b5e" title="In this instance, val= [theta, r]. For multivariate instances, it is stored columnwise...">evallog_nn</a> ( <span class="keyword">const</span> vec &amp;val ) <span class="keyword">const</span>;
-<a name="l00213"></a>00213                         <span class="keywordtype">double</span> <a class="code" href="classbdm_1_1egiw.html#41d72ba7b2abc8a9a4209ffa98ed5633" title="logarithm of the normalizing constant, ">lognc</a> () <span class="keyword">const</span>;
-<a name="l00214"></a><a class="code" href="classbdm_1_1egiw.html#8e610e95401a11baf34f65e16ecd87be">00214</a>                         <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1egiw.html#8e610e95401a11baf34f65e16ecd87be" title="Power of the density, used e.g. to flatten the density.">pow</a> ( <span class="keywordtype">double</span> p ) {V*=p;<a class="code" href="classbdm_1_1egiw.html#447eacf19d4f4083872686f044814dc4" title="Number of data records (degrees of freedom) of sufficient statistics.">nu</a>*=p;};
-<a name="l00215"></a>00215 
-<a name="l00218"></a>00218 
-<a name="l00219"></a>00219                         <a class="code" href="classldmat.html" title="Matrix stored in LD form, (commonly known as UD).">ldmat</a>&amp; _V() {<span class="keywordflow">return</span> V;}
-<a name="l00220"></a>00220                         <span class="keyword">const</span> <a class="code" href="classldmat.html" title="Matrix stored in LD form, (commonly known as UD).">ldmat</a>&amp; _V()<span class="keyword"> const </span>{<span class="keywordflow">return</span> V;}
-<a name="l00221"></a>00221                         <span class="keywordtype">double</span>&amp; _nu()  {<span class="keywordflow">return</span> <a class="code" href="classbdm_1_1egiw.html#447eacf19d4f4083872686f044814dc4" title="Number of data records (degrees of freedom) of sufficient statistics.">nu</a>;}
-<a name="l00222"></a>00222                         <span class="keyword">const</span> <span class="keywordtype">double</span>&amp; _nu()<span class="keyword"> const </span>{<span class="keywordflow">return</span> <a class="code" href="classbdm_1_1egiw.html#447eacf19d4f4083872686f044814dc4" title="Number of data records (degrees of freedom) of sufficient statistics.">nu</a>;}
-<a name="l00224"></a>00224         };
-<a name="l00225"></a>00225 
-<a name="l00234"></a><a class="code" href="classbdm_1_1eDirich.html">00234</a>         <span class="keyword">class </span><a class="code" href="classbdm_1_1eDirich.html" title="Dirichlet posterior density.">eDirich</a>: <span class="keyword">public</span> <a class="code" href="classbdm_1_1eEF.html" title="General conjugate exponential family posterior density.">eEF</a>
-<a name="l00235"></a>00235         {
-<a name="l00236"></a>00236                 <span class="keyword">protected</span>:
-<a name="l00238"></a><a class="code" href="classbdm_1_1eDirich.html#f25886a49b4667af61245de81c83b5d2">00238</a>                         vec <a class="code" href="classbdm_1_1eDirich.html#f25886a49b4667af61245de81c83b5d2" title="sufficient statistics">beta</a>;
-<a name="l00239"></a>00239                 <span class="keyword">public</span>:
-<a name="l00242"></a>00242 
-<a name="l00243"></a>00243                         <a class="code" href="classbdm_1_1eDirich.html" title="Dirichlet posterior density.">eDirich</a> () : <a class="code" href="classbdm_1_1eEF.html" title="General conjugate exponential family posterior density.">eEF</a> ( ) {};
-<a name="l00244"></a>00244                         <a class="code" href="classbdm_1_1eDirich.html" title="Dirichlet posterior density.">eDirich</a> ( <span class="keyword">const</span> <a class="code" href="classbdm_1_1eDirich.html" title="Dirichlet posterior density.">eDirich</a> &amp;D0 ) : <a class="code" href="classbdm_1_1eEF.html" title="General conjugate exponential family posterior density.">eEF</a> () {set_parameters ( D0.<a class="code" href="classbdm_1_1eDirich.html#f25886a49b4667af61245de81c83b5d2" title="sufficient statistics">beta</a> );};
-<a name="l00245"></a>00245                         eDirich ( <span class="keyword">const</span> vec &amp;beta0 ) {set_parameters ( beta0 );};
-<a name="l00246"></a>00246                         <span class="keywordtype">void</span> set_parameters ( <span class="keyword">const</span> vec &amp;beta0 )
-<a name="l00247"></a>00247                         {
-<a name="l00248"></a>00248                                 <a class="code" href="classbdm_1_1eDirich.html#f25886a49b4667af61245de81c83b5d2" title="sufficient statistics">beta</a>= beta0;
-<a name="l00249"></a>00249                                 <a class="code" href="classbdm_1_1epdf.html#16adac20ec7fe07e1ea0b27d917788ce" title="dimension of the random variable">dim</a> = <a class="code" href="classbdm_1_1eDirich.html#f25886a49b4667af61245de81c83b5d2" title="sufficient statistics">beta</a>.length();
-<a name="l00250"></a>00250                         }
-<a name="l00252"></a>00252 
-<a name="l00253"></a><a class="code" href="classbdm_1_1eDirich.html#3290613d31d58daa8a45a54b003871fc">00253</a>                         vec <a class="code" href="classbdm_1_1eDirich.html#3290613d31d58daa8a45a54b003871fc" title="Returns a sample,  from density .">sample</a>()<span class="keyword"> const </span>{it_error ( <span class="stringliteral">"Not implemented"</span> );<span class="keywordflow">return</span> vec_1 ( 0.0 );};
-<a name="l00254"></a><a class="code" href="classbdm_1_1eDirich.html#cb343355ec791298bb5a3404cd482fb6">00254</a>                         vec <a class="code" href="classbdm_1_1eDirich.html#cb343355ec791298bb5a3404cd482fb6" title="return expected value">mean</a>()<span class="keyword"> const </span>{<span class="keywordflow">return</span> <a class="code" href="classbdm_1_1eDirich.html#f25886a49b4667af61245de81c83b5d2" title="sufficient statistics">beta</a>/sum(<a class="code" href="classbdm_1_1eDirich.html#f25886a49b4667af61245de81c83b5d2" title="sufficient statistics">beta</a>);};
-<a name="l00255"></a><a class="code" href="classbdm_1_1eDirich.html#43c547a2507e233706f92712d8c2aacc">00255</a>                         vec <a class="code" href="classbdm_1_1eDirich.html#43c547a2507e233706f92712d8c2aacc" title="return expected variance (not covariance!)">variance</a>()<span class="keyword"> const </span>{<span class="keywordtype">double</span> gamma =sum(<a class="code" href="classbdm_1_1eDirich.html#f25886a49b4667af61245de81c83b5d2" title="sufficient statistics">beta</a>); <span class="keywordflow">return</span> elem_mult ( <a class="code" href="classbdm_1_1eDirich.html#f25886a49b4667af61245de81c83b5d2" title="sufficient statistics">beta</a>, ( <a class="code" href="classbdm_1_1eDirich.html#f25886a49b4667af61245de81c83b5d2" title="sufficient statistics">beta</a>+1 ) ) / ( gamma* ( gamma+1 ) );}
-<a name="l00257"></a><a class="code" href="classbdm_1_1eDirich.html#e09a24938e80c3d94b0ee842d1552318">00257</a>                         <span class="keywordtype">double</span> <a class="code" href="classbdm_1_1eDirich.html#e09a24938e80c3d94b0ee842d1552318" title="In this instance, val is ...">evallog_nn</a> ( <span class="keyword">const</span> vec &amp;val )<span class="keyword"> const</span>
-<a name="l00258"></a>00258 <span class="keyword">                        </span>{
-<a name="l00259"></a>00259                                 <span class="keywordtype">double</span> tmp; tmp= ( <a class="code" href="classbdm_1_1eDirich.html#f25886a49b4667af61245de81c83b5d2" title="sufficient statistics">beta</a>-1 ) *log ( val );               it_assert_debug ( std::isfinite ( tmp ),<span class="stringliteral">"Infinite value"</span> );
-<a name="l00260"></a>00260                                 <span class="keywordflow">return</span> tmp;
-<a name="l00261"></a>00261                         };
-<a name="l00262"></a><a class="code" href="classbdm_1_1eDirich.html#279a99f6266c82fe2273e83841f19eb2">00262</a>                         <span class="keywordtype">double</span> <a class="code" href="classbdm_1_1eDirich.html#279a99f6266c82fe2273e83841f19eb2" title="logarithm of the normalizing constant, ">lognc</a> ()<span class="keyword"> const</span>
-<a name="l00263"></a>00263 <span class="keyword">                        </span>{
-<a name="l00264"></a>00264                                 <span class="keywordtype">double</span> tmp;
-<a name="l00265"></a>00265                                 <span class="keywordtype">double</span> gam=sum ( <a class="code" href="classbdm_1_1eDirich.html#f25886a49b4667af61245de81c83b5d2" title="sufficient statistics">beta</a> );
-<a name="l00266"></a>00266                                 <span class="keywordtype">double</span> lgb=0.0;
-<a name="l00267"></a>00267                                 <span class="keywordflow">for</span> ( <span class="keywordtype">int</span> i=0;i&lt;<a class="code" href="classbdm_1_1eDirich.html#f25886a49b4667af61245de81c83b5d2" title="sufficient statistics">beta</a>.length();i++ ) {lgb+=lgamma ( <a class="code" href="classbdm_1_1eDirich.html#f25886a49b4667af61245de81c83b5d2" title="sufficient statistics">beta</a> ( i ) );}
-<a name="l00268"></a>00268                                 tmp= lgb-lgamma ( gam );
-<a name="l00269"></a>00269                                 it_assert_debug ( std::isfinite ( tmp ),<span class="stringliteral">"Infinite value"</span> );
-<a name="l00270"></a>00270                                 <span class="keywordflow">return</span> tmp;
-<a name="l00271"></a>00271                         };
-<a name="l00273"></a><a class="code" href="classbdm_1_1eDirich.html#175e0add26d2105c28d8121eefb9e324">00273</a>                         vec&amp; <a class="code" href="classbdm_1_1eDirich.html#175e0add26d2105c28d8121eefb9e324" title="access function">_beta</a>()  {<span class="keywordflow">return</span> <a class="code" href="classbdm_1_1eDirich.html#f25886a49b4667af61245de81c83b5d2" title="sufficient statistics">beta</a>;}
-<a name="l00275"></a>00275         };
-<a name="l00276"></a>00276 
-<a name="l00278"></a><a class="code" href="classbdm_1_1multiBM.html">00278</a>         <span class="keyword">class </span><a class="code" href="classbdm_1_1multiBM.html" title="Estimator for Multinomial density.">multiBM</a> : <span class="keyword">public</span> <a class="code" href="classbdm_1_1BMEF.html" title="Estimator for Exponential family.">BMEF</a>
-<a name="l00279"></a>00279         {
-<a name="l00280"></a>00280                 <span class="keyword">protected</span>:
-<a name="l00282"></a><a class="code" href="classbdm_1_1multiBM.html#9ecc6878abbd20eb8d8e43b6ab3f941a">00282</a>                         <a class="code" href="classbdm_1_1eDirich.html" title="Dirichlet posterior density.">eDirich</a> <a class="code" href="classbdm_1_1multiBM.html#9ecc6878abbd20eb8d8e43b6ab3f941a" title="Conjugate prior and posterior.">est</a>;
-<a name="l00284"></a><a class="code" href="classbdm_1_1multiBM.html#044263356944c92209eecd39a5187d25">00284</a>                         vec &amp;<a class="code" href="classbdm_1_1multiBM.html#044263356944c92209eecd39a5187d25" title="Pointer inside est to sufficient statistics.">beta</a>;
-<a name="l00285"></a>00285                 <span class="keyword">public</span>:
-<a name="l00287"></a><a class="code" href="classbdm_1_1multiBM.html#c4dd6d9522a8a605776d21bac9bd9daf">00287</a>                         <a class="code" href="classbdm_1_1multiBM.html#c4dd6d9522a8a605776d21bac9bd9daf" title="Default constructor.">multiBM</a> ( ) : <a class="code" href="classbdm_1_1BMEF.html" title="Estimator for Exponential family.">BMEF</a> ( ),<a class="code" href="classbdm_1_1multiBM.html#9ecc6878abbd20eb8d8e43b6ab3f941a" title="Conjugate prior and posterior.">est</a> ( ),<a class="code" href="classbdm_1_1multiBM.html#044263356944c92209eecd39a5187d25" title="Pointer inside est to sufficient statistics.">beta</a> ( <a class="code" href="classbdm_1_1multiBM.html#9ecc6878abbd20eb8d8e43b6ab3f941a" title="Conjugate prior and posterior.">est</a>._beta() )
-<a name="l00288"></a>00288                         {
-<a name="l00289"></a>00289                                 <span class="keywordflow">if</span> ( <a class="code" href="classbdm_1_1multiBM.html#044263356944c92209eecd39a5187d25" title="Pointer inside est to sufficient statistics.">beta</a>.length() &gt;0 ) {<a class="code" href="classbdm_1_1BMEF.html#06e7b3ac03e10017d4288c76888e2865" title="cached value of lognc() in the previous step (used in evaluation of ll )">last_lognc</a>=<a class="code" href="classbdm_1_1multiBM.html#9ecc6878abbd20eb8d8e43b6ab3f941a" title="Conjugate prior and posterior.">est</a>.<a class="code" href="classbdm_1_1eDirich.html#279a99f6266c82fe2273e83841f19eb2" title="logarithm of the normalizing constant, ">lognc</a>();}
-<a name="l00290"></a>00290                                 <span class="keywordflow">else</span>{<a class="code" href="classbdm_1_1BMEF.html#06e7b3ac03e10017d4288c76888e2865" title="cached value of lognc() in the previous step (used in evaluation of ll )">last_lognc</a>=0.0;}
-<a name="l00291"></a>00291                         }
-<a name="l00293"></a><a class="code" href="classbdm_1_1multiBM.html#c4378cf8037f6bed29c74eea63344b31">00293</a>                         <a class="code" href="classbdm_1_1multiBM.html#c4378cf8037f6bed29c74eea63344b31" title="Copy constructor.">multiBM</a> ( <span class="keyword">const</span> <a class="code" href="classbdm_1_1multiBM.html" title="Estimator for Multinomial density.">multiBM</a> &amp;B ) : <a class="code" href="classbdm_1_1BMEF.html" title="Estimator for Exponential family.">BMEF</a> ( B ),<a class="code" href="classbdm_1_1multiBM.html#9ecc6878abbd20eb8d8e43b6ab3f941a" title="Conjugate prior and posterior.">est</a> ( B.<a class="code" href="classbdm_1_1multiBM.html#9ecc6878abbd20eb8d8e43b6ab3f941a" title="Conjugate prior and posterior.">est</a> ),<a class="code" href="classbdm_1_1multiBM.html#044263356944c92209eecd39a5187d25" title="Pointer inside est to sufficient statistics.">beta</a> ( <a class="code" href="classbdm_1_1multiBM.html#9ecc6878abbd20eb8d8e43b6ab3f941a" title="Conjugate prior and posterior.">est</a>._beta() ) {}
-<a name="l00295"></a><a class="code" href="classbdm_1_1multiBM.html#dbe6b90d410dc062a233d1dc09eeba52">00295</a>                         <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1multiBM.html#dbe6b90d410dc062a233d1dc09eeba52" title="Sets sufficient statistics to match that of givefrom mB0.">set_statistics</a> ( <span class="keyword">const</span> <a class="code" href="classbdm_1_1BM.html" title="Bayesian Model of a system, i.e. all uncertainty is modeled by probabilities.">BM</a>* mB0 ) {<span class="keyword">const</span> <a class="code" href="classbdm_1_1multiBM.html" title="Estimator for Multinomial density.">multiBM</a>* mB=<span class="keyword">dynamic_cast&lt;</span><span class="keyword">const </span><a class="code" href="classbdm_1_1multiBM.html" title="Estimator for Multinomial density.">multiBM</a>*<span class="keyword">&gt;</span> ( mB0 ); <a class="code" href="classbdm_1_1multiBM.html#044263356944c92209eecd39a5187d25" title="Pointer inside est to sufficient statistics.">beta</a>=mB-&gt;<a class="code" href="classbdm_1_1multiBM.html#044263356944c92209eecd39a5187d25" title="Pointer inside est to sufficient statistics.">beta</a>;}
-<a name="l00296"></a><a class="code" href="classbdm_1_1multiBM.html#1e4bf41b61937fd80f34049742e23f95">00296</a>                         <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1multiBM.html#1e4bf41b61937fd80f34049742e23f95" title="Incremental Bayes rule.">bayes</a> ( <span class="keyword">const</span> vec &amp;dt )
-<a name="l00297"></a>00297                         {
-<a name="l00298"></a>00298                                 <span class="keywordflow">if</span> ( <a class="code" href="classbdm_1_1BMEF.html#1331865e10fb1ccef65bb4c47fa3be64" title="forgetting factor">frg</a>&lt;1.0 ) {<a class="code" href="classbdm_1_1multiBM.html#044263356944c92209eecd39a5187d25" title="Pointer inside est to sufficient statistics.">beta</a>*=<a class="code" href="classbdm_1_1BMEF.html#1331865e10fb1ccef65bb4c47fa3be64" title="forgetting factor">frg</a>;<a class="code" href="classbdm_1_1BMEF.html#06e7b3ac03e10017d4288c76888e2865" title="cached value of lognc() in the previous step (used in evaluation of ll )">last_lognc</a>=<a class="code" href="classbdm_1_1multiBM.html#9ecc6878abbd20eb8d8e43b6ab3f941a" title="Conjugate prior and posterior.">est</a>.<a class="code" href="classbdm_1_1eDirich.html#279a99f6266c82fe2273e83841f19eb2" title="logarithm of the normalizing constant, ">lognc</a>();}
-<a name="l00299"></a>00299                                 <a class="code" href="classbdm_1_1multiBM.html#044263356944c92209eecd39a5187d25" title="Pointer inside est to sufficient statistics.">beta</a>+=dt;
-<a name="l00300"></a>00300                                 <span class="keywordflow">if</span> ( <a class="code" href="classbdm_1_1BM.html#faff0ad12556fe7dc0e2807d4fd938ee" title="If true, the filter will compute likelihood of the data record and store it in ll...">evalll</a> ) {<a class="code" href="classbdm_1_1BM.html#4064b6559d962633e4372b12f4cd204a" title="Logarithm of marginalized data likelihood.">ll</a>=<a class="code" href="classbdm_1_1multiBM.html#9ecc6878abbd20eb8d8e43b6ab3f941a" title="Conjugate prior and posterior.">est</a>.<a class="code" href="classbdm_1_1eDirich.html#279a99f6266c82fe2273e83841f19eb2" title="logarithm of the normalizing constant, ">lognc</a>()-<a class="code" href="classbdm_1_1BMEF.html#06e7b3ac03e10017d4288c76888e2865" title="cached value of lognc() in the previous step (used in evaluation of ll )">last_lognc</a>;}
-<a name="l00301"></a>00301                         }
-<a name="l00302"></a><a class="code" href="classbdm_1_1multiBM.html#e157b607c1e3fa91d42aeea44458e2bf">00302</a>                         <span class="keywordtype">double</span> <a class="code" href="classbdm_1_1multiBM.html#e157b607c1e3fa91d42aeea44458e2bf">logpred</a> ( <span class="keyword">const</span> vec &amp;dt )<span class="keyword"> const</span>
-<a name="l00303"></a>00303 <span class="keyword">                        </span>{
-<a name="l00304"></a>00304                                 <a class="code" href="classbdm_1_1eDirich.html" title="Dirichlet posterior density.">eDirich</a> pred ( <a class="code" href="classbdm_1_1multiBM.html#9ecc6878abbd20eb8d8e43b6ab3f941a" title="Conjugate prior and posterior.">est</a> );
-<a name="l00305"></a>00305                                 vec &amp;<a class="code" href="classbdm_1_1multiBM.html#044263356944c92209eecd39a5187d25" title="Pointer inside est to sufficient statistics.">beta</a> = pred.<a class="code" href="classbdm_1_1eDirich.html#175e0add26d2105c28d8121eefb9e324" title="access function">_beta</a>();
-<a name="l00306"></a>00306 
-<a name="l00307"></a>00307                                 <span class="keywordtype">double</span> lll;
-<a name="l00308"></a>00308                                 <span class="keywordflow">if</span> ( <a class="code" href="classbdm_1_1BMEF.html#1331865e10fb1ccef65bb4c47fa3be64" title="forgetting factor">frg</a>&lt;1.0 )
-<a name="l00309"></a>00309                                         {beta*=<a class="code" href="classbdm_1_1BMEF.html#1331865e10fb1ccef65bb4c47fa3be64" title="forgetting factor">frg</a>;lll=pred.<a class="code" href="classbdm_1_1eDirich.html#279a99f6266c82fe2273e83841f19eb2" title="logarithm of the normalizing constant, ">lognc</a>();}
-<a name="l00310"></a>00310                                 <span class="keywordflow">else</span>
-<a name="l00311"></a>00311                                         <span class="keywordflow">if</span> ( <a class="code" href="classbdm_1_1BM.html#faff0ad12556fe7dc0e2807d4fd938ee" title="If true, the filter will compute likelihood of the data record and store it in ll...">evalll</a> ) {lll=<a class="code" href="classbdm_1_1BMEF.html#06e7b3ac03e10017d4288c76888e2865" title="cached value of lognc() in the previous step (used in evaluation of ll )">last_lognc</a>;}
-<a name="l00312"></a>00312                                         <span class="keywordflow">else</span>{lll=pred.<a class="code" href="classbdm_1_1eDirich.html#279a99f6266c82fe2273e83841f19eb2" title="logarithm of the normalizing constant, ">lognc</a>();}
-<a name="l00313"></a>00313 
-<a name="l00314"></a>00314                                 beta+=dt;
-<a name="l00315"></a>00315                                 <span class="keywordflow">return</span> pred.<a class="code" href="classbdm_1_1eDirich.html#279a99f6266c82fe2273e83841f19eb2" title="logarithm of the normalizing constant, ">lognc</a>()-lll;
-<a name="l00316"></a>00316                         }
-<a name="l00317"></a><a class="code" href="classbdm_1_1multiBM.html#aaeb18c989088feb8d26d300e4971732">00317</a>                         <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1multiBM.html#aaeb18c989088feb8d26d300e4971732" title="Flatten the posterior according to the given BMEF (of the same type!).">flatten</a> ( <span class="keyword">const</span> <a class="code" href="classbdm_1_1BMEF.html" title="Estimator for Exponential family.">BMEF</a>* B )
-<a name="l00318"></a>00318                         {
-<a name="l00319"></a>00319                                 <span class="keyword">const</span> <a class="code" href="classbdm_1_1multiBM.html" title="Estimator for Multinomial density.">multiBM</a>* E=<span class="keyword">dynamic_cast&lt;</span><span class="keyword">const </span><a class="code" href="classbdm_1_1multiBM.html" title="Estimator for Multinomial density.">multiBM</a>*<span class="keyword">&gt;</span> ( B );
-<a name="l00320"></a>00320                                 <span class="comment">// sum(beta) should be equal to sum(B.beta)</span>
-<a name="l00321"></a>00321                                 <span class="keyword">const</span> vec &amp;Eb=E-&gt;<a class="code" href="classbdm_1_1multiBM.html#044263356944c92209eecd39a5187d25" title="Pointer inside est to sufficient statistics.">beta</a>;<span class="comment">//const_cast&lt;multiBM*&gt; ( E )-&gt;_beta();</span>
-<a name="l00322"></a>00322                                 <a class="code" href="classbdm_1_1multiBM.html#044263356944c92209eecd39a5187d25" title="Pointer inside est to sufficient statistics.">beta</a>*= ( sum ( Eb ) /sum ( <a class="code" href="classbdm_1_1multiBM.html#044263356944c92209eecd39a5187d25" title="Pointer inside est to sufficient statistics.">beta</a> ) );
-<a name="l00323"></a>00323                                 <span class="keywordflow">if</span> ( <a class="code" href="classbdm_1_1BM.html#faff0ad12556fe7dc0e2807d4fd938ee" title="If true, the filter will compute likelihood of the data record and store it in ll...">evalll</a> ) {<a class="code" href="classbdm_1_1BMEF.html#06e7b3ac03e10017d4288c76888e2865" title="cached value of lognc() in the previous step (used in evaluation of ll )">last_lognc</a>=<a class="code" href="classbdm_1_1multiBM.html#9ecc6878abbd20eb8d8e43b6ab3f941a" title="Conjugate prior and posterior.">est</a>.<a class="code" href="classbdm_1_1eDirich.html#279a99f6266c82fe2273e83841f19eb2" title="logarithm of the normalizing constant, ">lognc</a>();}
-<a name="l00324"></a>00324                         }
-<a name="l00325"></a>00325                         <span class="keyword">const</span> <a class="code" href="classbdm_1_1epdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a>&amp; posterior()<span class="keyword"> const </span>{<span class="keywordflow">return</span> <a class="code" href="classbdm_1_1multiBM.html#9ecc6878abbd20eb8d8e43b6ab3f941a" title="Conjugate prior and posterior.">est</a>;};
-<a name="l00326"></a>00326                         <span class="keyword">const</span> eDirich* _e()<span class="keyword"> const </span>{<span class="keywordflow">return</span> &amp;<a class="code" href="classbdm_1_1multiBM.html#9ecc6878abbd20eb8d8e43b6ab3f941a" title="Conjugate prior and posterior.">est</a>;};
-<a name="l00327"></a>00327                         <span class="keywordtype">void</span> set_parameters ( <span class="keyword">const</span> vec &amp;beta0 )
-<a name="l00328"></a>00328                         {
-<a name="l00329"></a>00329                                 <a class="code" href="classbdm_1_1multiBM.html#9ecc6878abbd20eb8d8e43b6ab3f941a" title="Conjugate prior and posterior.">est</a>.set_parameters ( beta0 );
-<a name="l00330"></a>00330                                 <span class="keywordflow">if</span> ( <a class="code" href="classbdm_1_1BM.html#faff0ad12556fe7dc0e2807d4fd938ee" title="If true, the filter will compute likelihood of the data record and store it in ll...">evalll</a> ) {<a class="code" href="classbdm_1_1BMEF.html#06e7b3ac03e10017d4288c76888e2865" title="cached value of lognc() in the previous step (used in evaluation of ll )">last_lognc</a>=<a class="code" href="classbdm_1_1multiBM.html#9ecc6878abbd20eb8d8e43b6ab3f941a" title="Conjugate prior and posterior.">est</a>.lognc();}
-<a name="l00331"></a>00331                         }
-<a name="l00332"></a>00332         };
-<a name="l00333"></a>00333 
-<a name="l00343"></a><a class="code" href="classbdm_1_1egamma.html">00343</a>         <span class="keyword">class </span><a class="code" href="classbdm_1_1egamma.html" title="Gamma posterior density.">egamma</a> : <span class="keyword">public</span> <a class="code" href="classbdm_1_1eEF.html" title="General conjugate exponential family posterior density.">eEF</a>
-<a name="l00344"></a>00344         {
-<a name="l00345"></a>00345                 <span class="keyword">protected</span>:
-<a name="l00347"></a><a class="code" href="classbdm_1_1egamma.html#0901ec983e66b8337aaa506e13b122fa">00347</a>                         vec <a class="code" href="classbdm_1_1egamma.html#0901ec983e66b8337aaa506e13b122fa" title="Vector .">alpha</a>;
-<a name="l00349"></a><a class="code" href="classbdm_1_1egamma.html#457bfb1ccb2057df85073e519a15ccc1">00349</a>                         vec <a class="code" href="classbdm_1_1egamma.html#457bfb1ccb2057df85073e519a15ccc1" title="Vector .">beta</a>;
-<a name="l00350"></a>00350                 <span class="keyword">public</span> :
-<a name="l00353"></a>00353                         <a class="code" href="classbdm_1_1egamma.html" title="Gamma posterior density.">egamma</a> ( ) :<a class="code" href="classbdm_1_1eEF.html" title="General conjugate exponential family posterior density.">eEF</a> ( ), <a class="code" href="classbdm_1_1egamma.html#0901ec983e66b8337aaa506e13b122fa" title="Vector .">alpha</a> ( 0 ), <a class="code" href="classbdm_1_1egamma.html#457bfb1ccb2057df85073e519a15ccc1" title="Vector .">beta</a> ( 0 ) {};
-<a name="l00354"></a>00354                         <a class="code" href="classbdm_1_1egamma.html" title="Gamma posterior density.">egamma</a> ( <span class="keyword">const</span> vec &amp;a, <span class="keyword">const</span> vec &amp;b ) {set_parameters ( a, b );};
-<a name="l00355"></a>00355                         <span class="keywordtype">void</span> set_parameters ( <span class="keyword">const</span> vec &amp;a, <span class="keyword">const</span> vec &amp;b ) {<a class="code" href="classbdm_1_1egamma.html#0901ec983e66b8337aaa506e13b122fa" title="Vector .">alpha</a>=a,<a class="code" href="classbdm_1_1egamma.html#457bfb1ccb2057df85073e519a15ccc1" title="Vector .">beta</a>=b;<a class="code" href="classbdm_1_1epdf.html#16adac20ec7fe07e1ea0b27d917788ce" title="dimension of the random variable">dim</a> = <a class="code" href="classbdm_1_1egamma.html#0901ec983e66b8337aaa506e13b122fa" title="Vector .">alpha</a>.length();};
-<a name="l00357"></a>00357 
-<a name="l00358"></a>00358                         vec <a class="code" href="classbdm_1_1egamma.html#6ed82f0fd05f6002487256d8e75a0bbd" title="Returns a sample,  from density .">sample</a>() <span class="keyword">const</span>;
-<a name="l00360"></a>00360 <span class="comment">//      mat sample ( int N ) const;</span>
-<a name="l00361"></a>00361                         <span class="keywordtype">double</span> <a class="code" href="classbdm_1_1egamma.html#a8e11e5a580ff42a1b205974c60768c6" title="TODO: is it used anywhere?">evallog</a> ( <span class="keyword">const</span> vec &amp;val ) <span class="keyword">const</span>;
-<a name="l00362"></a>00362                         <span class="keywordtype">double</span> <a class="code" href="classbdm_1_1egamma.html#9a66cbd100e8520c769ccb3c451f86f8" title="logarithm of the normalizing constant, ">lognc</a> () <span class="keyword">const</span>;
-<a name="l00364"></a><a class="code" href="classbdm_1_1egamma.html#0865cb3d6339fdc7410806cf70a329ed">00364</a>                         vec&amp; <a class="code" href="classbdm_1_1egamma.html#0865cb3d6339fdc7410806cf70a329ed" title="Returns poiter to alpha and beta. Potentially dengerous: use with care!">_alpha</a>() {<span class="keywordflow">return</span> <a class="code" href="classbdm_1_1egamma.html#0901ec983e66b8337aaa506e13b122fa" title="Vector .">alpha</a>;}
-<a name="l00365"></a>00365                         vec&amp; _beta() {<span class="keywordflow">return</span> beta;}
-<a name="l00366"></a><a class="code" href="classbdm_1_1egamma.html#49d256c42cce14c6faa56ec242b57e85">00366</a>                         vec <a class="code" href="classbdm_1_1egamma.html#49d256c42cce14c6faa56ec242b57e85" title="return expected value">mean</a>()<span class="keyword"> const </span>{<span class="keywordflow">return</span> elem_div ( <a class="code" href="classbdm_1_1egamma.html#0901ec983e66b8337aaa506e13b122fa" title="Vector .">alpha</a>,beta );}
-<a name="l00367"></a><a class="code" href="classbdm_1_1egamma.html#36986cc01917cd0796fadc17125bdec1">00367</a>                         vec <a class="code" href="classbdm_1_1egamma.html#36986cc01917cd0796fadc17125bdec1" title="return expected variance (not covariance!)">variance</a>()<span class="keyword"> const </span>{<span class="keywordflow">return</span> elem_div ( <a class="code" href="classbdm_1_1egamma.html#0901ec983e66b8337aaa506e13b122fa" title="Vector .">alpha</a>,elem_mult ( beta,beta ) ); }
-<a name="l00368"></a>00368         };
-<a name="l00369"></a>00369 
-<a name="l00386"></a><a class="code" href="classbdm_1_1eigamma.html">00386</a>         <span class="keyword">class </span><a class="code" href="classbdm_1_1eigamma.html" title="Inverse-Gamma posterior density.">eigamma</a> : <span class="keyword">public</span> <a class="code" href="classbdm_1_1egamma.html" title="Gamma posterior density.">egamma</a>
-<a name="l00387"></a>00387         {
-<a name="l00388"></a>00388                 <span class="keyword">protected</span>:
-<a name="l00389"></a>00389                 <span class="keyword">public</span> :
-<a name="l00394"></a>00394 
-<a name="l00395"></a><a class="code" href="classbdm_1_1eigamma.html#3aff7bf25ddac27731c60826fcfd878f">00395</a>                         vec <a class="code" href="classbdm_1_1eigamma.html#3aff7bf25ddac27731c60826fcfd878f" title="Returns a sample,  from density .">sample</a>()<span class="keyword"> const </span>{<span class="keywordflow">return</span> 1.0/<a class="code" href="classbdm_1_1eigamma.html#3aff7bf25ddac27731c60826fcfd878f" title="Returns a sample,  from density .">egamma::sample</a>();};
-<a name="l00397"></a><a class="code" href="classbdm_1_1eigamma.html#46cecb295edbabd28120cb0f6f572bcb">00397</a>                         vec <a class="code" href="classbdm_1_1eigamma.html#46cecb295edbabd28120cb0f6f572bcb" title="Returns poiter to alpha and beta. Potentially dangerous: use with care!">mean</a>()<span class="keyword"> const </span>{<span class="keywordflow">return</span> elem_div ( <a class="code" href="classbdm_1_1egamma.html#457bfb1ccb2057df85073e519a15ccc1" title="Vector .">beta</a>,<a class="code" href="classbdm_1_1egamma.html#0901ec983e66b8337aaa506e13b122fa" title="Vector .">alpha</a>-1 );}
-<a name="l00398"></a><a class="code" href="classbdm_1_1eigamma.html#c2c696f8c668e9f65392c9449f6a5133">00398</a>                         vec <a class="code" href="classbdm_1_1eigamma.html#c2c696f8c668e9f65392c9449f6a5133" title="return expected variance (not covariance!)">variance</a>()<span class="keyword"> const </span>{vec mea=<a class="code" href="classbdm_1_1eigamma.html#46cecb295edbabd28120cb0f6f572bcb" title="Returns poiter to alpha and beta. Potentially dangerous: use with care!">mean</a>(); <span class="keywordflow">return</span> elem_div ( elem_mult ( mea,mea ),<a class="code" href="classbdm_1_1egamma.html#0901ec983e66b8337aaa506e13b122fa" title="Vector .">alpha</a>-2 );}
-<a name="l00399"></a>00399         };
-<a name="l00400"></a>00400         <span class="comment">/*</span>
-<a name="l00402"></a>00402 <span class="comment">        class emix : public epdf {</span>
-<a name="l00403"></a>00403 <span class="comment">        protected:</span>
-<a name="l00404"></a>00404 <span class="comment">                int n;</span>
-<a name="l00405"></a>00405 <span class="comment">                vec &amp;w;</span>
-<a name="l00406"></a>00406 <span class="comment">                Array&lt;epdf*&gt; Coms;</span>
-<a name="l00407"></a>00407 <span class="comment">        public:</span>
-<a name="l00409"></a>00409 <span class="comment">                emix ( const RV &amp;rv, vec &amp;w0): epdf(rv), n(w0.length()), w(w0), Coms(n) {};</span>
-<a name="l00410"></a>00410 <span class="comment">                void set_parameters( int &amp;i, double wi, epdf* ep){w(i)=wi;Coms(i)=ep;}</span>
-<a name="l00411"></a>00411 <span class="comment">                vec mean(){vec pom; for(int i=0;i&lt;n;i++){pom+=Coms(i)-&gt;mean()*w(i);} return pom;};</span>
-<a name="l00412"></a>00412 <span class="comment">                vec sample() {it_error ( "Not implemented" );return 0;}</span>
-<a name="l00413"></a>00413 <span class="comment">        };</span>
-<a name="l00414"></a>00414 <span class="comment">        */</span>
-<a name="l00415"></a>00415 
-<a name="l00417"></a>00417 
-<a name="l00418"></a><a class="code" href="classbdm_1_1euni.html">00418</a>         <span class="keyword">class </span><a class="code" href="classbdm_1_1euni.html" title="Uniform distributed density on a rectangular support.">euni</a>: <span class="keyword">public</span> <a class="code" href="classbdm_1_1epdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a>
-<a name="l00419"></a>00419         {
-<a name="l00420"></a>00420                 <span class="keyword">protected</span>:
-<a name="l00422"></a><a class="code" href="classbdm_1_1euni.html#ff7ce6a2ef5ef0015bbd1398bed12f32">00422</a>                         vec <a class="code" href="classbdm_1_1euni.html#ff7ce6a2ef5ef0015bbd1398bed12f32" title="lower bound on support">low</a>;
-<a name="l00424"></a><a class="code" href="classbdm_1_1euni.html#cfad2dea4a62db6872bda8abd75f0de1">00424</a>                         vec <a class="code" href="classbdm_1_1euni.html#cfad2dea4a62db6872bda8abd75f0de1" title="upper bound on support">high</a>;
-<a name="l00426"></a><a class="code" href="classbdm_1_1euni.html#d3c27e331f90c754d80228108de8ed4c">00426</a>                         vec <a class="code" href="classbdm_1_1euni.html#d3c27e331f90c754d80228108de8ed4c" title="internal">distance</a>;
-<a name="l00428"></a><a class="code" href="classbdm_1_1euni.html#31bb13e8449a8eff35246d46dae35c20">00428</a>                         <span class="keywordtype">double</span> <a class="code" href="classbdm_1_1euni.html#31bb13e8449a8eff35246d46dae35c20" title="normalizing coefficients">nk</a>;
-<a name="l00430"></a><a class="code" href="classbdm_1_1euni.html#3e63be48dd58659663ca60cd18700476">00430</a>                         <span class="keywordtype">double</span> <a class="code" href="classbdm_1_1euni.html#3e63be48dd58659663ca60cd18700476" title="cache of log( nk )">lnk</a>;
-<a name="l00431"></a>00431                 <span class="keyword">public</span>:
-<a name="l00434"></a>00434                         <a class="code" href="classbdm_1_1euni.html" title="Uniform distributed density on a rectangular support.">euni</a> ( ) :<a class="code" href="classbdm_1_1epdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a> ( ) {}
-<a name="l00435"></a>00435                         <a class="code" href="classbdm_1_1euni.html" title="Uniform distributed density on a rectangular support.">euni</a> ( <span class="keyword">const</span> vec &amp;low0, <span class="keyword">const</span> vec &amp;high0 ) {set_parameters ( low0,high0 );}
-<a name="l00436"></a>00436                         <span class="keywordtype">void</span> set_parameters ( <span class="keyword">const</span> vec &amp;low0, <span class="keyword">const</span> vec &amp;high0 )
-<a name="l00437"></a>00437                         {
-<a name="l00438"></a>00438                                 <a class="code" href="classbdm_1_1euni.html#d3c27e331f90c754d80228108de8ed4c" title="internal">distance</a> = high0-low0;
-<a name="l00439"></a>00439                                 it_assert_debug ( min ( <a class="code" href="classbdm_1_1euni.html#d3c27e331f90c754d80228108de8ed4c" title="internal">distance</a> ) &gt;0.0,<span class="stringliteral">"bad support"</span> );
-<a name="l00440"></a>00440                                 <a class="code" href="classbdm_1_1euni.html#ff7ce6a2ef5ef0015bbd1398bed12f32" title="lower bound on support">low</a> = low0;
-<a name="l00441"></a>00441                                 <a class="code" href="classbdm_1_1euni.html#cfad2dea4a62db6872bda8abd75f0de1" title="upper bound on support">high</a> = high0;
-<a name="l00442"></a>00442                                 <a class="code" href="classbdm_1_1euni.html#31bb13e8449a8eff35246d46dae35c20" title="normalizing coefficients">nk</a> = prod ( 1.0/<a class="code" href="classbdm_1_1euni.html#d3c27e331f90c754d80228108de8ed4c" title="internal">distance</a> );
-<a name="l00443"></a>00443                                 <a class="code" href="classbdm_1_1euni.html#3e63be48dd58659663ca60cd18700476" title="cache of log( nk )">lnk</a> = log ( <a class="code" href="classbdm_1_1euni.html#31bb13e8449a8eff35246d46dae35c20" title="normalizing coefficients">nk</a> );
-<a name="l00444"></a>00444                                 <a class="code" href="classbdm_1_1epdf.html#16adac20ec7fe07e1ea0b27d917788ce" title="dimension of the random variable">dim</a> = <a class="code" href="classbdm_1_1euni.html#ff7ce6a2ef5ef0015bbd1398bed12f32" title="lower bound on support">low</a>.length();
-<a name="l00445"></a>00445                         }
-<a name="l00447"></a>00447 
-<a name="l00448"></a>00448                         <span class="keywordtype">double</span> eval ( <span class="keyword">const</span> vec &amp;val )<span class="keyword"> const  </span>{<span class="keywordflow">return</span> <a class="code" href="classbdm_1_1euni.html#31bb13e8449a8eff35246d46dae35c20" title="normalizing coefficients">nk</a>;}
-<a name="l00449"></a><a class="code" href="classbdm_1_1euni.html#caa07b8307bd793d5339d6583e0aba81">00449</a>                         <span class="keywordtype">double</span> <a class="code" href="classbdm_1_1euni.html#caa07b8307bd793d5339d6583e0aba81" title="Compute log-probability of argument val.">evallog</a> ( <span class="keyword">const</span> vec &amp;val )<span class="keyword"> const  </span>{<span class="keywordflow">return</span> <a class="code" href="classbdm_1_1euni.html#3e63be48dd58659663ca60cd18700476" title="cache of log( nk )">lnk</a>;}
-<a name="l00450"></a><a class="code" href="classbdm_1_1euni.html#fc5df80359ead2918384b2004ce67194">00450</a>                         vec <a class="code" href="classbdm_1_1euni.html#fc5df80359ead2918384b2004ce67194" title="Returns a sample,  from density .">sample</a>()<span class="keyword"> const</span>
-<a name="l00451"></a>00451 <span class="keyword">                        </span>{
-<a name="l00452"></a>00452                                 vec smp ( <a class="code" href="classbdm_1_1epdf.html#16adac20ec7fe07e1ea0b27d917788ce" title="dimension of the random variable">dim</a> );
-<a name="l00453"></a>00453 <span class="preprocessor">#pragma omp critical</span>
-<a name="l00454"></a>00454 <span class="preprocessor"></span>                                UniRNG.sample_vector ( <a class="code" href="classbdm_1_1epdf.html#16adac20ec7fe07e1ea0b27d917788ce" title="dimension of the random variable">dim</a> ,smp );
-<a name="l00455"></a>00455                                 <span class="keywordflow">return</span> <a class="code" href="classbdm_1_1euni.html#ff7ce6a2ef5ef0015bbd1398bed12f32" title="lower bound on support">low</a>+elem_mult ( <a class="code" href="classbdm_1_1euni.html#d3c27e331f90c754d80228108de8ed4c" title="internal">distance</a>,smp );
-<a name="l00456"></a>00456                         }
-<a name="l00458"></a><a class="code" href="classbdm_1_1euni.html#46caa8c13aba2e6228f964208918b226">00458</a>                         vec <a class="code" href="classbdm_1_1euni.html#46caa8c13aba2e6228f964208918b226" title="set values of low and high ">mean</a>()<span class="keyword"> const </span>{<span class="keywordflow">return</span> ( <a class="code" href="classbdm_1_1euni.html#cfad2dea4a62db6872bda8abd75f0de1" title="upper bound on support">high</a>-<a class="code" href="classbdm_1_1euni.html#ff7ce6a2ef5ef0015bbd1398bed12f32" title="lower bound on support">low</a> ) /2.0;}
-<a name="l00459"></a><a class="code" href="classbdm_1_1euni.html#951f932155111f6053c980f672b4c22c">00459</a>                         vec <a class="code" href="classbdm_1_1euni.html#951f932155111f6053c980f672b4c22c" title="return expected variance (not covariance!)">variance</a>()<span class="keyword"> const </span>{<span class="keywordflow">return</span> ( pow ( <a class="code" href="classbdm_1_1euni.html#cfad2dea4a62db6872bda8abd75f0de1" title="upper bound on support">high</a>,2 ) +pow ( <a class="code" href="classbdm_1_1euni.html#ff7ce6a2ef5ef0015bbd1398bed12f32" title="lower bound on support">low</a>,2 ) +elem_mult ( <a class="code" href="classbdm_1_1euni.html#cfad2dea4a62db6872bda8abd75f0de1" title="upper bound on support">high</a>,<a class="code" href="classbdm_1_1euni.html#ff7ce6a2ef5ef0015bbd1398bed12f32" title="lower bound on support">low</a> ) ) /3.0;}
-<a name="l00460"></a>00460         };
-<a name="l00461"></a>00461 
+<a name="l00127"></a>00127                         <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1enorm.html#61bd470764020bea6e1ed35000f259e6" title="This method arrange instance properties according the data stored in the Setting...">from_setting</a>(<span class="keyword">const</span> Setting &amp;root);
+<a name="l00129"></a>00129 
+<a name="l00132"></a>00132 
+<a name="l00134"></a>00134                         <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1enorm.html#d2e0d3a1e30ab3ab04df2d0c43ae74a2" title="dupdate in exponential form (not really handy)">dupdate</a> ( mat &amp;v,<span class="keywordtype">double</span> nu=1.0 );
+<a name="l00135"></a>00135 
+<a name="l00136"></a>00136                         vec <a class="code" href="classbdm_1_1enorm.html#e1a48f52351ec3a349bd443b713b1766" title="Returns a sample,  from density .">sample</a>() <span class="keyword">const</span>;
+<a name="l00137"></a>00137                         mat <a class="code" href="classbdm_1_1enorm.html#e1a48f52351ec3a349bd443b713b1766" title="Returns a sample,  from density .">sample</a> ( <span class="keywordtype">int</span> N ) <span class="keyword">const</span>;
+<a name="l00138"></a>00138                         <span class="keywordtype">double</span> <a class="code" href="classbdm_1_1enorm.html#e13aeed5b543b2179bacdc4fa2ae47a3" title="Evaluate normalized log-probability.">evallog_nn</a> ( <span class="keyword">const</span> vec &amp;val ) <span class="keyword">const</span>;
+<a name="l00139"></a>00139                         <span class="keywordtype">double</span> <a class="code" href="classbdm_1_1enorm.html#25785343aff102cc5df1cab08ba16d32" title="logarithm of the normalizing constant, ">lognc</a> () <span class="keyword">const</span>;
+<a name="l00140"></a><a class="code" href="classbdm_1_1enorm.html#b2fa2915c35366392fe9bb022ca1a600">00140</a>                         vec <a class="code" href="classbdm_1_1enorm.html#b2fa2915c35366392fe9bb022ca1a600" title="return expected value">mean</a>()<span class="keyword"> const </span>{<span class="keywordflow">return</span> <a class="code" href="classbdm_1_1enorm.html#c702a194720853570d08b65482f842c7" title="mean value">mu</a>;}
+<a name="l00141"></a><a class="code" href="classbdm_1_1enorm.html#729c75ef0fa8abae03d58ad1f81e6773">00141</a>                         vec <a class="code" href="classbdm_1_1enorm.html#729c75ef0fa8abae03d58ad1f81e6773" title="return expected variance (not covariance!)">variance</a>()<span class="keyword"> const </span>{<span class="keywordflow">return</span> diag ( <a class="code" href="classbdm_1_1enorm.html#2d92dde696b2a7a5b10ddef5d22ba2c2" title="Covariance matrix in decomposed form.">R</a>.to_mat() );}
+<a name="l00142"></a>00142 <span class="comment">//      mlnorm&lt;sq_T&gt;* condition ( const RV &amp;rvn ) const ; &lt;=========== fails to cmpile. Why?</span>
+<a name="l00143"></a>00143                         <a class="code" href="classbdm_1_1mpdf.html" title="Conditional probability density, e.g. modeling some dependencies.">mpdf</a>* <a class="code" href="classbdm_1_1enorm.html#baea4d49c657342b58297d68cda16d26" title="Return conditional density on the given RV, the remaining rvs will be in conditioning...">condition</a> ( <span class="keyword">const</span> <a class="code" href="classbdm_1_1RV.html" title="Class representing variables, most often random variables.">RV</a> &amp;rvn ) <span class="keyword">const</span> ;
+<a name="l00144"></a>00144                         <a class="code" href="classbdm_1_1enorm.html" title="Gaussian density with positive definite (decomposed) covariance matrix.">enorm&lt;sq_T&gt;</a>* <a class="code" href="classbdm_1_1enorm.html#c2996bdaffad38fbe0fc776db54c9d80" title="Return marginal density on the given RV, the remainig rvs are intergrated out.">marginal</a> ( <span class="keyword">const</span> <a class="code" href="classbdm_1_1RV.html" title="Class representing variables, most often random variables.">RV</a> &amp;<a class="code" href="classbdm_1_1epdf.html#62c5b8ff71d9ebe6cd58d3c342eb1dc8" title="Description of the random variable.">rv</a> ) <span class="keyword">const</span>;
+<a name="l00145"></a>00145 <span class="comment">//                      epdf* marginal ( const RV &amp;rv ) const;</span>
+<a name="l00147"></a>00147 <span class="comment"></span>
+<a name="l00150"></a>00150 
+<a name="l00151"></a>00151                         vec&amp; _mu() {<span class="keywordflow">return</span> <a class="code" href="classbdm_1_1enorm.html#c702a194720853570d08b65482f842c7" title="mean value">mu</a>;}
+<a name="l00152"></a>00152                         <span class="keywordtype">void</span> set_mu ( <span class="keyword">const</span> vec mu0 ) { <a class="code" href="classbdm_1_1enorm.html#c702a194720853570d08b65482f842c7" title="mean value">mu</a>=mu0;}
+<a name="l00153"></a>00153                         sq_T&amp; _R() {<span class="keywordflow">return</span> <a class="code" href="classbdm_1_1enorm.html#2d92dde696b2a7a5b10ddef5d22ba2c2" title="Covariance matrix in decomposed form.">R</a>;}
+<a name="l00154"></a>00154                         <span class="keyword">const</span> sq_T&amp; _R()<span class="keyword"> const </span>{<span class="keywordflow">return</span> <a class="code" href="classbdm_1_1enorm.html#2d92dde696b2a7a5b10ddef5d22ba2c2" title="Covariance matrix in decomposed form.">R</a>;}
+<a name="l00156"></a>00156 
+<a name="l00157"></a>00157         };
+<a name="l00158"></a>00158 
+<a name="l00165"></a><a class="code" href="classbdm_1_1egiw.html">00165</a>         <span class="keyword">class </span><a class="code" href="classbdm_1_1egiw.html" title="Gauss-inverse-Wishart density stored in LD form.">egiw</a> : <span class="keyword">public</span> <a class="code" href="classbdm_1_1eEF.html" title="General conjugate exponential family posterior density.">eEF</a>
+<a name="l00166"></a>00166         {
+<a name="l00167"></a>00167                 <span class="keyword">protected</span>:
+<a name="l00169"></a><a class="code" href="classbdm_1_1egiw.html#ae56852845c6af176fd9017dbebbbd52">00169</a>                         <a class="code" href="classldmat.html" title="Matrix stored in LD form, (commonly known as UD).">ldmat</a> <a class="code" href="classbdm_1_1egiw.html#ae56852845c6af176fd9017dbebbbd52" title="Extended information matrix of sufficient statistics.">V</a>;
+<a name="l00171"></a><a class="code" href="classbdm_1_1egiw.html#447eacf19d4f4083872686f044814dc4">00171</a>                         <span class="keywordtype">double</span> <a class="code" href="classbdm_1_1egiw.html#447eacf19d4f4083872686f044814dc4" title="Number of data records (degrees of freedom) of sufficient statistics.">nu</a>;
+<a name="l00173"></a><a class="code" href="classbdm_1_1egiw.html#23e4d78bea7e98840f3da30e76a2b57a">00173</a>                         <span class="keywordtype">int</span> <a class="code" href="classbdm_1_1egiw.html#23e4d78bea7e98840f3da30e76a2b57a" title="Dimension of the output.">dimx</a>;
+<a name="l00175"></a><a class="code" href="classbdm_1_1egiw.html#322414c32d9a21a006a5aab0311f64fd">00175</a>                         <span class="keywordtype">int</span> <a class="code" href="classbdm_1_1egiw.html#322414c32d9a21a006a5aab0311f64fd" title="Dimension of the regressor.">nPsi</a>;
+<a name="l00176"></a>00176                 <span class="keyword">public</span>:
+<a name="l00179"></a>00179                         <a class="code" href="classbdm_1_1egiw.html" title="Gauss-inverse-Wishart density stored in LD form.">egiw</a>() :<a class="code" href="classbdm_1_1eEF.html" title="General conjugate exponential family posterior density.">eEF</a>() {};
+<a name="l00180"></a>00180                         <a class="code" href="classbdm_1_1egiw.html" title="Gauss-inverse-Wishart density stored in LD form.">egiw</a> ( <span class="keywordtype">int</span> dimx0, <a class="code" href="classldmat.html" title="Matrix stored in LD form, (commonly known as UD).">ldmat</a> V0, <span class="keywordtype">double</span> nu0=-1.0 ) :<a class="code" href="classbdm_1_1eEF.html" title="General conjugate exponential family posterior density.">eEF</a>() {set_parameters ( dimx0,V0, nu0 );};
+<a name="l00181"></a>00181 
+<a name="l00182"></a>00182                         <span class="keywordtype">void</span> set_parameters ( <span class="keywordtype">int</span> dimx0, <a class="code" href="classldmat.html" title="Matrix stored in LD form, (commonly known as UD).">ldmat</a> V0, <span class="keywordtype">double</span> nu0=-1.0 )
+<a name="l00183"></a>00183                         {
+<a name="l00184"></a>00184                                 <a class="code" href="classbdm_1_1egiw.html#23e4d78bea7e98840f3da30e76a2b57a" title="Dimension of the output.">dimx</a>=dimx0;
+<a name="l00185"></a>00185                                 <a class="code" href="classbdm_1_1egiw.html#322414c32d9a21a006a5aab0311f64fd" title="Dimension of the regressor.">nPsi</a> = V0.<a class="code" href="group__math.html#g96dfb21865db4f5bd36fa70f9b0b1163" title="access function">rows</a>()-<a class="code" href="classbdm_1_1egiw.html#23e4d78bea7e98840f3da30e76a2b57a" title="Dimension of the output.">dimx</a>;
+<a name="l00186"></a>00186                                 <a class="code" href="classbdm_1_1epdf.html#16adac20ec7fe07e1ea0b27d917788ce" title="dimension of the random variable">dim</a> = <a class="code" href="classbdm_1_1egiw.html#23e4d78bea7e98840f3da30e76a2b57a" title="Dimension of the output.">dimx</a>* ( <a class="code" href="classbdm_1_1egiw.html#23e4d78bea7e98840f3da30e76a2b57a" title="Dimension of the output.">dimx</a>+<a class="code" href="classbdm_1_1egiw.html#322414c32d9a21a006a5aab0311f64fd" title="Dimension of the regressor.">nPsi</a> ); <span class="comment">// size(R) + size(Theta)</span>
+<a name="l00187"></a>00187 
+<a name="l00188"></a>00188                                 <a class="code" href="classbdm_1_1egiw.html#ae56852845c6af176fd9017dbebbbd52" title="Extended information matrix of sufficient statistics.">V</a>=V0;
+<a name="l00189"></a>00189                                 <span class="keywordflow">if</span> ( nu0&lt;0 )
+<a name="l00190"></a>00190                                 {
+<a name="l00191"></a>00191                                         <a class="code" href="classbdm_1_1egiw.html#447eacf19d4f4083872686f044814dc4" title="Number of data records (degrees of freedom) of sufficient statistics.">nu</a> = 0.1 +<a class="code" href="classbdm_1_1egiw.html#322414c32d9a21a006a5aab0311f64fd" title="Dimension of the regressor.">nPsi</a> +2*<a class="code" href="classbdm_1_1egiw.html#23e4d78bea7e98840f3da30e76a2b57a" title="Dimension of the output.">dimx</a> +2; <span class="comment">// +2 assures finite expected value of R</span>
+<a name="l00192"></a>00192                                         <span class="comment">// terms before that are sufficient for finite normalization</span>
+<a name="l00193"></a>00193                                 }
+<a name="l00194"></a>00194                                 <span class="keywordflow">else</span>
+<a name="l00195"></a>00195                                 {
+<a name="l00196"></a>00196                                         <a class="code" href="classbdm_1_1egiw.html#447eacf19d4f4083872686f044814dc4" title="Number of data records (degrees of freedom) of sufficient statistics.">nu</a>=nu0;
+<a name="l00197"></a>00197                                 }
+<a name="l00198"></a>00198                         }
+<a name="l00200"></a>00200 
+<a name="l00201"></a>00201                         vec <a class="code" href="classbdm_1_1egiw.html#920f21548b7a3723923dd108fe514c61" title="Returns a sample,  from density .">sample</a>() <span class="keyword">const</span>;
+<a name="l00202"></a>00202                         vec <a class="code" href="classbdm_1_1egiw.html#df70c05f918c3a6f86d60f10c1fd6ba2" title="return expected value">mean</a>() <span class="keyword">const</span>;
+<a name="l00203"></a>00203                         vec <a class="code" href="classbdm_1_1egiw.html#c1ecc406613cc2341225dc10c3d3b46a" title="return expected variance (not covariance!)">variance</a>() <span class="keyword">const</span>;
+<a name="l00204"></a>00204 
+<a name="l00206"></a>00206                         vec <a class="code" href="classbdm_1_1egiw.html#66d2ba9295c306012b309efcc9e516f0" title="LS estimate of .">est_theta</a>() <span class="keyword">const</span>;
+<a name="l00207"></a>00207 
+<a name="l00209"></a>00209                         <a class="code" href="classldmat.html" title="Matrix stored in LD form, (commonly known as UD).">ldmat</a> <a class="code" href="classbdm_1_1egiw.html#88c321a2051d1afdbb31a098896a717b" title="Covariance of the LS estimate.">est_theta_cov</a>() <span class="keyword">const</span>;
+<a name="l00210"></a>00210 
+<a name="l00211"></a>00211                         <span class="keywordtype">void</span> mean_mat ( mat &amp;M, mat&amp;R ) <span class="keyword">const</span>;
+<a name="l00213"></a>00213                         <span class="keywordtype">double</span> <a class="code" href="classbdm_1_1egiw.html#bfb8e7c619b34ad804a73bff71742b5e" title="In this instance, val= [theta, r]. For multivariate instances, it is stored columnwise...">evallog_nn</a> ( <span class="keyword">const</span> vec &amp;val ) <span class="keyword">const</span>;
+<a name="l00214"></a>00214                         <span class="keywordtype">double</span> <a class="code" href="classbdm_1_1egiw.html#41d72ba7b2abc8a9a4209ffa98ed5633" title="logarithm of the normalizing constant, ">lognc</a> () <span class="keyword">const</span>;
+<a name="l00215"></a><a class="code" href="classbdm_1_1egiw.html#8e610e95401a11baf34f65e16ecd87be">00215</a>                         <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1egiw.html#8e610e95401a11baf34f65e16ecd87be" title="Power of the density, used e.g. to flatten the density.">pow</a> ( <span class="keywordtype">double</span> p ) {V*=p;<a class="code" href="classbdm_1_1egiw.html#447eacf19d4f4083872686f044814dc4" title="Number of data records (degrees of freedom) of sufficient statistics.">nu</a>*=p;};
+<a name="l00216"></a>00216 
+<a name="l00219"></a>00219 
+<a name="l00220"></a>00220                         <a class="code" href="classldmat.html" title="Matrix stored in LD form, (commonly known as UD).">ldmat</a>&amp; _V() {<span class="keywordflow">return</span> V;}
+<a name="l00221"></a>00221                         <span class="keyword">const</span> <a class="code" href="classldmat.html" title="Matrix stored in LD form, (commonly known as UD).">ldmat</a>&amp; _V()<span class="keyword"> const </span>{<span class="keywordflow">return</span> V;}
+<a name="l00222"></a>00222                         <span class="keywordtype">double</span>&amp; _nu()  {<span class="keywordflow">return</span> <a class="code" href="classbdm_1_1egiw.html#447eacf19d4f4083872686f044814dc4" title="Number of data records (degrees of freedom) of sufficient statistics.">nu</a>;}
+<a name="l00223"></a>00223                         <span class="keyword">const</span> <span class="keywordtype">double</span>&amp; _nu()<span class="keyword"> const </span>{<span class="keywordflow">return</span> <a class="code" href="classbdm_1_1egiw.html#447eacf19d4f4083872686f044814dc4" title="Number of data records (degrees of freedom) of sufficient statistics.">nu</a>;}
+<a name="l00225"></a>00225         };
+<a name="l00226"></a>00226 
+<a name="l00235"></a><a class="code" href="classbdm_1_1eDirich.html">00235</a>         <span class="keyword">class </span><a class="code" href="classbdm_1_1eDirich.html" title="Dirichlet posterior density.">eDirich</a>: <span class="keyword">public</span> <a class="code" href="classbdm_1_1eEF.html" title="General conjugate exponential family posterior density.">eEF</a>
+<a name="l00236"></a>00236         {
+<a name="l00237"></a>00237                 <span class="keyword">protected</span>:
+<a name="l00239"></a><a class="code" href="classbdm_1_1eDirich.html#f25886a49b4667af61245de81c83b5d2">00239</a>                         vec <a class="code" href="classbdm_1_1eDirich.html#f25886a49b4667af61245de81c83b5d2" title="sufficient statistics">beta</a>;
+<a name="l00240"></a>00240                 <span class="keyword">public</span>:
+<a name="l00243"></a>00243 
+<a name="l00244"></a>00244                         <a class="code" href="classbdm_1_1eDirich.html" title="Dirichlet posterior density.">eDirich</a> () : <a class="code" href="classbdm_1_1eEF.html" title="General conjugate exponential family posterior density.">eEF</a> ( ) {};
+<a name="l00245"></a>00245                         <a class="code" href="classbdm_1_1eDirich.html" title="Dirichlet posterior density.">eDirich</a> ( <span class="keyword">const</span> <a class="code" href="classbdm_1_1eDirich.html" title="Dirichlet posterior density.">eDirich</a> &amp;D0 ) : <a class="code" href="classbdm_1_1eEF.html" title="General conjugate exponential family posterior density.">eEF</a> () {set_parameters ( D0.<a class="code" href="classbdm_1_1eDirich.html#f25886a49b4667af61245de81c83b5d2" title="sufficient statistics">beta</a> );};
+<a name="l00246"></a>00246                         eDirich ( <span class="keyword">const</span> vec &amp;beta0 ) {set_parameters ( beta0 );};
+<a name="l00247"></a>00247                         <span class="keywordtype">void</span> set_parameters ( <span class="keyword">const</span> vec &amp;beta0 )
+<a name="l00248"></a>00248                         {
+<a name="l00249"></a>00249                                 <a class="code" href="classbdm_1_1eDirich.html#f25886a49b4667af61245de81c83b5d2" title="sufficient statistics">beta</a>= beta0;
+<a name="l00250"></a>00250                                 <a class="code" href="classbdm_1_1epdf.html#16adac20ec7fe07e1ea0b27d917788ce" title="dimension of the random variable">dim</a> = <a class="code" href="classbdm_1_1eDirich.html#f25886a49b4667af61245de81c83b5d2" title="sufficient statistics">beta</a>.length();
+<a name="l00251"></a>00251                         }
+<a name="l00253"></a>00253 
+<a name="l00254"></a><a class="code" href="classbdm_1_1eDirich.html#3290613d31d58daa8a45a54b003871fc">00254</a>                         vec <a class="code" href="classbdm_1_1eDirich.html#3290613d31d58daa8a45a54b003871fc" title="Returns a sample,  from density .">sample</a>()<span class="keyword"> const </span>{it_error ( <span class="stringliteral">"Not implemented"</span> );<span class="keywordflow">return</span> vec_1 ( 0.0 );};
+<a name="l00255"></a><a class="code" href="classbdm_1_1eDirich.html#cb343355ec791298bb5a3404cd482fb6">00255</a>                         vec <a class="code" href="classbdm_1_1eDirich.html#cb343355ec791298bb5a3404cd482fb6" title="return expected value">mean</a>()<span class="keyword"> const </span>{<span class="keywordflow">return</span> <a class="code" href="classbdm_1_1eDirich.html#f25886a49b4667af61245de81c83b5d2" title="sufficient statistics">beta</a>/sum(<a class="code" href="classbdm_1_1eDirich.html#f25886a49b4667af61245de81c83b5d2" title="sufficient statistics">beta</a>);};
+<a name="l00256"></a><a class="code" href="classbdm_1_1eDirich.html#43c547a2507e233706f92712d8c2aacc">00256</a>                         vec <a class="code" href="classbdm_1_1eDirich.html#43c547a2507e233706f92712d8c2aacc" title="return expected variance (not covariance!)">variance</a>()<span class="keyword"> const </span>{<span class="keywordtype">double</span> gamma =sum(<a class="code" href="classbdm_1_1eDirich.html#f25886a49b4667af61245de81c83b5d2" title="sufficient statistics">beta</a>); <span class="keywordflow">return</span> elem_mult ( <a class="code" href="classbdm_1_1eDirich.html#f25886a49b4667af61245de81c83b5d2" title="sufficient statistics">beta</a>, ( <a class="code" href="classbdm_1_1eDirich.html#f25886a49b4667af61245de81c83b5d2" title="sufficient statistics">beta</a>+1 ) ) / ( gamma* ( gamma+1 ) );}
+<a name="l00258"></a><a class="code" href="classbdm_1_1eDirich.html#e09a24938e80c3d94b0ee842d1552318">00258</a>                         <span class="keywordtype">double</span> <a class="code" href="classbdm_1_1eDirich.html#e09a24938e80c3d94b0ee842d1552318" title="In this instance, val is ...">evallog_nn</a> ( <span class="keyword">const</span> vec &amp;val )<span class="keyword"> const</span>
+<a name="l00259"></a>00259 <span class="keyword">                        </span>{
+<a name="l00260"></a>00260                                 <span class="keywordtype">double</span> tmp; tmp= ( <a class="code" href="classbdm_1_1eDirich.html#f25886a49b4667af61245de81c83b5d2" title="sufficient statistics">beta</a>-1 ) *log ( val );               it_assert_debug ( std::isfinite ( tmp ),<span class="stringliteral">"Infinite value"</span> );
+<a name="l00261"></a>00261                                 <span class="keywordflow">return</span> tmp;
+<a name="l00262"></a>00262                         };
+<a name="l00263"></a><a class="code" href="classbdm_1_1eDirich.html#279a99f6266c82fe2273e83841f19eb2">00263</a>                         <span class="keywordtype">double</span> <a class="code" href="classbdm_1_1eDirich.html#279a99f6266c82fe2273e83841f19eb2" title="logarithm of the normalizing constant, ">lognc</a> ()<span class="keyword"> const</span>
+<a name="l00264"></a>00264 <span class="keyword">                        </span>{
+<a name="l00265"></a>00265                                 <span class="keywordtype">double</span> tmp;
+<a name="l00266"></a>00266                                 <span class="keywordtype">double</span> gam=sum ( <a class="code" href="classbdm_1_1eDirich.html#f25886a49b4667af61245de81c83b5d2" title="sufficient statistics">beta</a> );
+<a name="l00267"></a>00267                                 <span class="keywordtype">double</span> lgb=0.0;
+<a name="l00268"></a>00268                                 <span class="keywordflow">for</span> ( <span class="keywordtype">int</span> i=0;i&lt;<a class="code" href="classbdm_1_1eDirich.html#f25886a49b4667af61245de81c83b5d2" title="sufficient statistics">beta</a>.length();i++ ) {lgb+=lgamma ( <a class="code" href="classbdm_1_1eDirich.html#f25886a49b4667af61245de81c83b5d2" title="sufficient statistics">beta</a> ( i ) );}
+<a name="l00269"></a>00269                                 tmp= lgb-lgamma ( gam );
+<a name="l00270"></a>00270                                 it_assert_debug ( std::isfinite ( tmp ),<span class="stringliteral">"Infinite value"</span> );
+<a name="l00271"></a>00271                                 <span class="keywordflow">return</span> tmp;
+<a name="l00272"></a>00272                         };
+<a name="l00274"></a><a class="code" href="classbdm_1_1eDirich.html#175e0add26d2105c28d8121eefb9e324">00274</a>                         vec&amp; <a class="code" href="classbdm_1_1eDirich.html#175e0add26d2105c28d8121eefb9e324" title="access function">_beta</a>()  {<span class="keywordflow">return</span> <a class="code" href="classbdm_1_1eDirich.html#f25886a49b4667af61245de81c83b5d2" title="sufficient statistics">beta</a>;}
+<a name="l00276"></a>00276         };
+<a name="l00277"></a>00277 
+<a name="l00279"></a><a class="code" href="classbdm_1_1multiBM.html">00279</a>         <span class="keyword">class </span><a class="code" href="classbdm_1_1multiBM.html" title="Estimator for Multinomial density.">multiBM</a> : <span class="keyword">public</span> <a class="code" href="classbdm_1_1BMEF.html" title="Estimator for Exponential family.">BMEF</a>
+<a name="l00280"></a>00280         {
+<a name="l00281"></a>00281                 <span class="keyword">protected</span>:
+<a name="l00283"></a><a class="code" href="classbdm_1_1multiBM.html#9ecc6878abbd20eb8d8e43b6ab3f941a">00283</a>                         <a class="code" href="classbdm_1_1eDirich.html" title="Dirichlet posterior density.">eDirich</a> <a class="code" href="classbdm_1_1multiBM.html#9ecc6878abbd20eb8d8e43b6ab3f941a" title="Conjugate prior and posterior.">est</a>;
+<a name="l00285"></a><a class="code" href="classbdm_1_1multiBM.html#044263356944c92209eecd39a5187d25">00285</a>                         vec &amp;<a class="code" href="classbdm_1_1multiBM.html#044263356944c92209eecd39a5187d25" title="Pointer inside est to sufficient statistics.">beta</a>;
+<a name="l00286"></a>00286                 <span class="keyword">public</span>:
+<a name="l00288"></a><a class="code" href="classbdm_1_1multiBM.html#c4dd6d9522a8a605776d21bac9bd9daf">00288</a>                         <a class="code" href="classbdm_1_1multiBM.html#c4dd6d9522a8a605776d21bac9bd9daf" title="Default constructor.">multiBM</a> ( ) : <a class="code" href="classbdm_1_1BMEF.html" title="Estimator for Exponential family.">BMEF</a> ( ),<a class="code" href="classbdm_1_1multiBM.html#9ecc6878abbd20eb8d8e43b6ab3f941a" title="Conjugate prior and posterior.">est</a> ( ),<a class="code" href="classbdm_1_1multiBM.html#044263356944c92209eecd39a5187d25" title="Pointer inside est to sufficient statistics.">beta</a> ( <a class="code" href="classbdm_1_1multiBM.html#9ecc6878abbd20eb8d8e43b6ab3f941a" title="Conjugate prior and posterior.">est</a>._beta() )
+<a name="l00289"></a>00289                         {
+<a name="l00290"></a>00290                                 <span class="keywordflow">if</span> ( <a class="code" href="classbdm_1_1multiBM.html#044263356944c92209eecd39a5187d25" title="Pointer inside est to sufficient statistics.">beta</a>.length() &gt;0 ) {<a class="code" href="classbdm_1_1BMEF.html#06e7b3ac03e10017d4288c76888e2865" title="cached value of lognc() in the previous step (used in evaluation of ll )">last_lognc</a>=<a class="code" href="classbdm_1_1multiBM.html#9ecc6878abbd20eb8d8e43b6ab3f941a" title="Conjugate prior and posterior.">est</a>.<a class="code" href="classbdm_1_1eDirich.html#279a99f6266c82fe2273e83841f19eb2" title="logarithm of the normalizing constant, ">lognc</a>();}
+<a name="l00291"></a>00291                                 <span class="keywordflow">else</span>{<a class="code" href="classbdm_1_1BMEF.html#06e7b3ac03e10017d4288c76888e2865" title="cached value of lognc() in the previous step (used in evaluation of ll )">last_lognc</a>=0.0;}
+<a name="l00292"></a>00292                         }
+<a name="l00294"></a><a class="code" href="classbdm_1_1multiBM.html#c4378cf8037f6bed29c74eea63344b31">00294</a>                         <a class="code" href="classbdm_1_1multiBM.html#c4378cf8037f6bed29c74eea63344b31" title="Copy constructor.">multiBM</a> ( <span class="keyword">const</span> <a class="code" href="classbdm_1_1multiBM.html" title="Estimator for Multinomial density.">multiBM</a> &amp;B ) : <a class="code" href="classbdm_1_1BMEF.html" title="Estimator for Exponential family.">BMEF</a> ( B ),<a class="code" href="classbdm_1_1multiBM.html#9ecc6878abbd20eb8d8e43b6ab3f941a" title="Conjugate prior and posterior.">est</a> ( B.<a class="code" href="classbdm_1_1multiBM.html#9ecc6878abbd20eb8d8e43b6ab3f941a" title="Conjugate prior and posterior.">est</a> ),<a class="code" href="classbdm_1_1multiBM.html#044263356944c92209eecd39a5187d25" title="Pointer inside est to sufficient statistics.">beta</a> ( <a class="code" href="classbdm_1_1multiBM.html#9ecc6878abbd20eb8d8e43b6ab3f941a" title="Conjugate prior and posterior.">est</a>._beta() ) {}
+<a name="l00296"></a><a class="code" href="classbdm_1_1multiBM.html#dbe6b90d410dc062a233d1dc09eeba52">00296</a>                         <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1multiBM.html#dbe6b90d410dc062a233d1dc09eeba52" title="Sets sufficient statistics to match that of givefrom mB0.">set_statistics</a> ( <span class="keyword">const</span> <a class="code" href="classbdm_1_1BM.html" title="Bayesian Model of a system, i.e. all uncertainty is modeled by probabilities.">BM</a>* mB0 ) {<span class="keyword">const</span> <a class="code" href="classbdm_1_1multiBM.html" title="Estimator for Multinomial density.">multiBM</a>* mB=<span class="keyword">dynamic_cast&lt;</span><span class="keyword">const </span><a class="code" href="classbdm_1_1multiBM.html" title="Estimator for Multinomial density.">multiBM</a>*<span class="keyword">&gt;</span> ( mB0 ); <a class="code" href="classbdm_1_1multiBM.html#044263356944c92209eecd39a5187d25" title="Pointer inside est to sufficient statistics.">beta</a>=mB-&gt;<a class="code" href="classbdm_1_1multiBM.html#044263356944c92209eecd39a5187d25" title="Pointer inside est to sufficient statistics.">beta</a>;}
+<a name="l00297"></a><a class="code" href="classbdm_1_1multiBM.html#1e4bf41b61937fd80f34049742e23f95">00297</a>                         <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1multiBM.html#1e4bf41b61937fd80f34049742e23f95" title="Incremental Bayes rule.">bayes</a> ( <span class="keyword">const</span> vec &amp;dt )
+<a name="l00298"></a>00298                         {
+<a name="l00299"></a>00299                                 <span class="keywordflow">if</span> ( <a class="code" href="classbdm_1_1BMEF.html#1331865e10fb1ccef65bb4c47fa3be64" title="forgetting factor">frg</a>&lt;1.0 ) {<a class="code" href="classbdm_1_1multiBM.html#044263356944c92209eecd39a5187d25" title="Pointer inside est to sufficient statistics.">beta</a>*=<a class="code" href="classbdm_1_1BMEF.html#1331865e10fb1ccef65bb4c47fa3be64" title="forgetting factor">frg</a>;<a class="code" href="classbdm_1_1BMEF.html#06e7b3ac03e10017d4288c76888e2865" title="cached value of lognc() in the previous step (used in evaluation of ll )">last_lognc</a>=<a class="code" href="classbdm_1_1multiBM.html#9ecc6878abbd20eb8d8e43b6ab3f941a" title="Conjugate prior and posterior.">est</a>.<a class="code" href="classbdm_1_1eDirich.html#279a99f6266c82fe2273e83841f19eb2" title="logarithm of the normalizing constant, ">lognc</a>();}
+<a name="l00300"></a>00300                                 <a class="code" href="classbdm_1_1multiBM.html#044263356944c92209eecd39a5187d25" title="Pointer inside est to sufficient statistics.">beta</a>+=dt;
+<a name="l00301"></a>00301                                 <span class="keywordflow">if</span> ( <a class="code" href="classbdm_1_1BM.html#faff0ad12556fe7dc0e2807d4fd938ee" title="If true, the filter will compute likelihood of the data record and store it in ll...">evalll</a> ) {<a class="code" href="classbdm_1_1BM.html#4064b6559d962633e4372b12f4cd204a" title="Logarithm of marginalized data likelihood.">ll</a>=<a class="code" href="classbdm_1_1multiBM.html#9ecc6878abbd20eb8d8e43b6ab3f941a" title="Conjugate prior and posterior.">est</a>.<a class="code" href="classbdm_1_1eDirich.html#279a99f6266c82fe2273e83841f19eb2" title="logarithm of the normalizing constant, ">lognc</a>()-<a class="code" href="classbdm_1_1BMEF.html#06e7b3ac03e10017d4288c76888e2865" title="cached value of lognc() in the previous step (used in evaluation of ll )">last_lognc</a>;}
+<a name="l00302"></a>00302                         }
+<a name="l00303"></a><a class="code" href="classbdm_1_1multiBM.html#e157b607c1e3fa91d42aeea44458e2bf">00303</a>                         <span class="keywordtype">double</span> <a class="code" href="classbdm_1_1multiBM.html#e157b607c1e3fa91d42aeea44458e2bf">logpred</a> ( <span class="keyword">const</span> vec &amp;dt )<span class="keyword"> const</span>
+<a name="l00304"></a>00304 <span class="keyword">                        </span>{
+<a name="l00305"></a>00305                                 <a class="code" href="classbdm_1_1eDirich.html" title="Dirichlet posterior density.">eDirich</a> pred ( <a class="code" href="classbdm_1_1multiBM.html#9ecc6878abbd20eb8d8e43b6ab3f941a" title="Conjugate prior and posterior.">est</a> );
+<a name="l00306"></a>00306                                 vec &amp;<a class="code" href="classbdm_1_1multiBM.html#044263356944c92209eecd39a5187d25" title="Pointer inside est to sufficient statistics.">beta</a> = pred.<a class="code" href="classbdm_1_1eDirich.html#175e0add26d2105c28d8121eefb9e324" title="access function">_beta</a>();
+<a name="l00307"></a>00307 
+<a name="l00308"></a>00308                                 <span class="keywordtype">double</span> lll;
+<a name="l00309"></a>00309                                 <span class="keywordflow">if</span> ( <a class="code" href="classbdm_1_1BMEF.html#1331865e10fb1ccef65bb4c47fa3be64" title="forgetting factor">frg</a>&lt;1.0 )
+<a name="l00310"></a>00310                                         {beta*=<a class="code" href="classbdm_1_1BMEF.html#1331865e10fb1ccef65bb4c47fa3be64" title="forgetting factor">frg</a>;lll=pred.<a class="code" href="classbdm_1_1eDirich.html#279a99f6266c82fe2273e83841f19eb2" title="logarithm of the normalizing constant, ">lognc</a>();}
+<a name="l00311"></a>00311                                 <span class="keywordflow">else</span>
+<a name="l00312"></a>00312                                         <span class="keywordflow">if</span> ( <a class="code" href="classbdm_1_1BM.html#faff0ad12556fe7dc0e2807d4fd938ee" title="If true, the filter will compute likelihood of the data record and store it in ll...">evalll</a> ) {lll=<a class="code" href="classbdm_1_1BMEF.html#06e7b3ac03e10017d4288c76888e2865" title="cached value of lognc() in the previous step (used in evaluation of ll )">last_lognc</a>;}
+<a name="l00313"></a>00313                                         <span class="keywordflow">else</span>{lll=pred.<a class="code" href="classbdm_1_1eDirich.html#279a99f6266c82fe2273e83841f19eb2" title="logarithm of the normalizing constant, ">lognc</a>();}
+<a name="l00314"></a>00314 
+<a name="l00315"></a>00315                                 beta+=dt;
+<a name="l00316"></a>00316                                 <span class="keywordflow">return</span> pred.<a class="code" href="classbdm_1_1eDirich.html#279a99f6266c82fe2273e83841f19eb2" title="logarithm of the normalizing constant, ">lognc</a>()-lll;
+<a name="l00317"></a>00317                         }
+<a name="l00318"></a><a class="code" href="classbdm_1_1multiBM.html#aaeb18c989088feb8d26d300e4971732">00318</a>                         <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1multiBM.html#aaeb18c989088feb8d26d300e4971732" title="Flatten the posterior according to the given BMEF (of the same type!).">flatten</a> ( <span class="keyword">const</span> <a class="code" href="classbdm_1_1BMEF.html" title="Estimator for Exponential family.">BMEF</a>* B )
+<a name="l00319"></a>00319                         {
+<a name="l00320"></a>00320                                 <span class="keyword">const</span> <a class="code" href="classbdm_1_1multiBM.html" title="Estimator for Multinomial density.">multiBM</a>* E=<span class="keyword">dynamic_cast&lt;</span><span class="keyword">const </span><a class="code" href="classbdm_1_1multiBM.html" title="Estimator for Multinomial density.">multiBM</a>*<span class="keyword">&gt;</span> ( B );
+<a name="l00321"></a>00321                                 <span class="comment">// sum(beta) should be equal to sum(B.beta)</span>
+<a name="l00322"></a>00322                                 <span class="keyword">const</span> vec &amp;Eb=E-&gt;<a class="code" href="classbdm_1_1multiBM.html#044263356944c92209eecd39a5187d25" title="Pointer inside est to sufficient statistics.">beta</a>;<span class="comment">//const_cast&lt;multiBM*&gt; ( E )-&gt;_beta();</span>
+<a name="l00323"></a>00323                                 <a class="code" href="classbdm_1_1multiBM.html#044263356944c92209eecd39a5187d25" title="Pointer inside est to sufficient statistics.">beta</a>*= ( sum ( Eb ) /sum ( <a class="code" href="classbdm_1_1multiBM.html#044263356944c92209eecd39a5187d25" title="Pointer inside est to sufficient statistics.">beta</a> ) );
+<a name="l00324"></a>00324                                 <span class="keywordflow">if</span> ( <a class="code" href="classbdm_1_1BM.html#faff0ad12556fe7dc0e2807d4fd938ee" title="If true, the filter will compute likelihood of the data record and store it in ll...">evalll</a> ) {<a class="code" href="classbdm_1_1BMEF.html#06e7b3ac03e10017d4288c76888e2865" title="cached value of lognc() in the previous step (used in evaluation of ll )">last_lognc</a>=<a class="code" href="classbdm_1_1multiBM.html#9ecc6878abbd20eb8d8e43b6ab3f941a" title="Conjugate prior and posterior.">est</a>.<a class="code" href="classbdm_1_1eDirich.html#279a99f6266c82fe2273e83841f19eb2" title="logarithm of the normalizing constant, ">lognc</a>();}
+<a name="l00325"></a>00325                         }
+<a name="l00326"></a>00326                         <span class="keyword">const</span> <a class="code" href="classbdm_1_1epdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a>&amp; posterior()<span class="keyword"> const </span>{<span class="keywordflow">return</span> <a class="code" href="classbdm_1_1multiBM.html#9ecc6878abbd20eb8d8e43b6ab3f941a" title="Conjugate prior and posterior.">est</a>;};
+<a name="l00327"></a>00327                         <span class="keyword">const</span> eDirich* _e()<span class="keyword"> const </span>{<span class="keywordflow">return</span> &amp;<a class="code" href="classbdm_1_1multiBM.html#9ecc6878abbd20eb8d8e43b6ab3f941a" title="Conjugate prior and posterior.">est</a>;};
+<a name="l00328"></a>00328                         <span class="keywordtype">void</span> set_parameters ( <span class="keyword">const</span> vec &amp;beta0 )
+<a name="l00329"></a>00329                         {
+<a name="l00330"></a>00330                                 <a class="code" href="classbdm_1_1multiBM.html#9ecc6878abbd20eb8d8e43b6ab3f941a" title="Conjugate prior and posterior.">est</a>.set_parameters ( beta0 );
+<a name="l00331"></a>00331                                 <span class="keywordflow">if</span> ( <a class="code" href="classbdm_1_1BM.html#faff0ad12556fe7dc0e2807d4fd938ee" title="If true, the filter will compute likelihood of the data record and store it in ll...">evalll</a> ) {<a class="code" href="classbdm_1_1BMEF.html#06e7b3ac03e10017d4288c76888e2865" title="cached value of lognc() in the previous step (used in evaluation of ll )">last_lognc</a>=<a class="code" href="classbdm_1_1multiBM.html#9ecc6878abbd20eb8d8e43b6ab3f941a" title="Conjugate prior and posterior.">est</a>.lognc();}
+<a name="l00332"></a>00332                         }
+<a name="l00333"></a>00333         };
+<a name="l00334"></a>00334 
+<a name="l00344"></a><a class="code" href="classbdm_1_1egamma.html">00344</a>         <span class="keyword">class </span><a class="code" href="classbdm_1_1egamma.html" title="Gamma posterior density.">egamma</a> : <span class="keyword">public</span> <a class="code" href="classbdm_1_1eEF.html" title="General conjugate exponential family posterior density.">eEF</a>
+<a name="l00345"></a>00345         {
+<a name="l00346"></a>00346                 <span class="keyword">protected</span>:
+<a name="l00348"></a><a class="code" href="classbdm_1_1egamma.html#0901ec983e66b8337aaa506e13b122fa">00348</a>                         vec <a class="code" href="classbdm_1_1egamma.html#0901ec983e66b8337aaa506e13b122fa" title="Vector .">alpha</a>;
+<a name="l00350"></a><a class="code" href="classbdm_1_1egamma.html#457bfb1ccb2057df85073e519a15ccc1">00350</a>                         vec <a class="code" href="classbdm_1_1egamma.html#457bfb1ccb2057df85073e519a15ccc1" title="Vector .">beta</a>;
+<a name="l00351"></a>00351                 <span class="keyword">public</span> :
+<a name="l00354"></a>00354                         <a class="code" href="classbdm_1_1egamma.html" title="Gamma posterior density.">egamma</a> ( ) :<a class="code" href="classbdm_1_1eEF.html" title="General conjugate exponential family posterior density.">eEF</a> ( ), <a class="code" href="classbdm_1_1egamma.html#0901ec983e66b8337aaa506e13b122fa" title="Vector .">alpha</a> ( 0 ), <a class="code" href="classbdm_1_1egamma.html#457bfb1ccb2057df85073e519a15ccc1" title="Vector .">beta</a> ( 0 ) {};
+<a name="l00355"></a>00355                         <a class="code" href="classbdm_1_1egamma.html" title="Gamma posterior density.">egamma</a> ( <span class="keyword">const</span> vec &amp;a, <span class="keyword">const</span> vec &amp;b ) {set_parameters ( a, b );};
+<a name="l00356"></a>00356                         <span class="keywordtype">void</span> set_parameters ( <span class="keyword">const</span> vec &amp;a, <span class="keyword">const</span> vec &amp;b ) {<a class="code" href="classbdm_1_1egamma.html#0901ec983e66b8337aaa506e13b122fa" title="Vector .">alpha</a>=a,<a class="code" href="classbdm_1_1egamma.html#457bfb1ccb2057df85073e519a15ccc1" title="Vector .">beta</a>=b;<a class="code" href="classbdm_1_1epdf.html#16adac20ec7fe07e1ea0b27d917788ce" title="dimension of the random variable">dim</a> = <a class="code" href="classbdm_1_1egamma.html#0901ec983e66b8337aaa506e13b122fa" title="Vector .">alpha</a>.length();};
+<a name="l00358"></a>00358 
+<a name="l00359"></a>00359                         vec <a class="code" href="classbdm_1_1egamma.html#6ed82f0fd05f6002487256d8e75a0bbd" title="Returns a sample,  from density .">sample</a>() <span class="keyword">const</span>;
+<a name="l00361"></a>00361 <span class="comment">//      mat sample ( int N ) const;</span>
+<a name="l00362"></a>00362                         <span class="keywordtype">double</span> <a class="code" href="classbdm_1_1egamma.html#a8e11e5a580ff42a1b205974c60768c6" title="TODO: is it used anywhere?">evallog</a> ( <span class="keyword">const</span> vec &amp;val ) <span class="keyword">const</span>;
+<a name="l00363"></a>00363                         <span class="keywordtype">double</span> <a class="code" href="classbdm_1_1egamma.html#9a66cbd100e8520c769ccb3c451f86f8" title="logarithm of the normalizing constant, ">lognc</a> () <span class="keyword">const</span>;
+<a name="l00365"></a><a class="code" href="classbdm_1_1egamma.html#0865cb3d6339fdc7410806cf70a329ed">00365</a>                         vec&amp; <a class="code" href="classbdm_1_1egamma.html#0865cb3d6339fdc7410806cf70a329ed" title="Returns poiter to alpha and beta. Potentially dengerous: use with care!">_alpha</a>() {<span class="keywordflow">return</span> <a class="code" href="classbdm_1_1egamma.html#0901ec983e66b8337aaa506e13b122fa" title="Vector .">alpha</a>;}
+<a name="l00366"></a>00366                         vec&amp; _beta() {<span class="keywordflow">return</span> beta;}
+<a name="l00367"></a><a class="code" href="classbdm_1_1egamma.html#49d256c42cce14c6faa56ec242b57e85">00367</a>                         vec <a class="code" href="classbdm_1_1egamma.html#49d256c42cce14c6faa56ec242b57e85" title="return expected value">mean</a>()<span class="keyword"> const </span>{<span class="keywordflow">return</span> elem_div ( <a class="code" href="classbdm_1_1egamma.html#0901ec983e66b8337aaa506e13b122fa" title="Vector .">alpha</a>,beta );}
+<a name="l00368"></a><a class="code" href="classbdm_1_1egamma.html#36986cc01917cd0796fadc17125bdec1">00368</a>                         vec <a class="code" href="classbdm_1_1egamma.html#36986cc01917cd0796fadc17125bdec1" title="return expected variance (not covariance!)">variance</a>()<span class="keyword"> const </span>{<span class="keywordflow">return</span> elem_div ( <a class="code" href="classbdm_1_1egamma.html#0901ec983e66b8337aaa506e13b122fa" title="Vector .">alpha</a>,elem_mult ( beta,beta ) ); }
+<a name="l00369"></a>00369         };
+<a name="l00370"></a>00370 
+<a name="l00387"></a><a class="code" href="classbdm_1_1eigamma.html">00387</a>         <span class="keyword">class </span><a class="code" href="classbdm_1_1eigamma.html" title="Inverse-Gamma posterior density.">eigamma</a> : <span class="keyword">public</span> <a class="code" href="classbdm_1_1egamma.html" title="Gamma posterior density.">egamma</a>
+<a name="l00388"></a>00388         {
+<a name="l00389"></a>00389                 <span class="keyword">protected</span>:
+<a name="l00390"></a>00390                 <span class="keyword">public</span> :
+<a name="l00395"></a>00395 
+<a name="l00396"></a><a class="code" href="classbdm_1_1eigamma.html#3aff7bf25ddac27731c60826fcfd878f">00396</a>                         vec <a class="code" href="classbdm_1_1eigamma.html#3aff7bf25ddac27731c60826fcfd878f" title="Returns a sample,  from density .">sample</a>()<span class="keyword"> const </span>{<span class="keywordflow">return</span> 1.0/<a class="code" href="classbdm_1_1eigamma.html#3aff7bf25ddac27731c60826fcfd878f" title="Returns a sample,  from density .">egamma::sample</a>();};
+<a name="l00398"></a><a class="code" href="classbdm_1_1eigamma.html#46cecb295edbabd28120cb0f6f572bcb">00398</a>                         vec <a class="code" href="classbdm_1_1eigamma.html#46cecb295edbabd28120cb0f6f572bcb" title="Returns poiter to alpha and beta. Potentially dangerous: use with care!">mean</a>()<span class="keyword"> const </span>{<span class="keywordflow">return</span> elem_div ( <a class="code" href="classbdm_1_1egamma.html#457bfb1ccb2057df85073e519a15ccc1" title="Vector .">beta</a>,<a class="code" href="classbdm_1_1egamma.html#0901ec983e66b8337aaa506e13b122fa" title="Vector .">alpha</a>-1 );}
+<a name="l00399"></a><a class="code" href="classbdm_1_1eigamma.html#c2c696f8c668e9f65392c9449f6a5133">00399</a>                         vec <a class="code" href="classbdm_1_1eigamma.html#c2c696f8c668e9f65392c9449f6a5133" title="return expected variance (not covariance!)">variance</a>()<span class="keyword"> const </span>{vec mea=<a class="code" href="classbdm_1_1eigamma.html#46cecb295edbabd28120cb0f6f572bcb" title="Returns poiter to alpha and beta. Potentially dangerous: use with care!">mean</a>(); <span class="keywordflow">return</span> elem_div ( elem_mult ( mea,mea ),<a class="code" href="classbdm_1_1egamma.html#0901ec983e66b8337aaa506e13b122fa" title="Vector .">alpha</a>-2 );}
+<a name="l00400"></a>00400         };
+<a name="l00401"></a>00401         <span class="comment">/*</span>
+<a name="l00403"></a>00403 <span class="comment">        class emix : public epdf {</span>
+<a name="l00404"></a>00404 <span class="comment">        protected:</span>
+<a name="l00405"></a>00405 <span class="comment">                int n;</span>
+<a name="l00406"></a>00406 <span class="comment">                vec &amp;w;</span>
+<a name="l00407"></a>00407 <span class="comment">                Array&lt;epdf*&gt; Coms;</span>
+<a name="l00408"></a>00408 <span class="comment">        public:</span>
+<a name="l00410"></a>00410 <span class="comment">                emix ( const RV &amp;rv, vec &amp;w0): epdf(rv), n(w0.length()), w(w0), Coms(n) {};</span>
+<a name="l00411"></a>00411 <span class="comment">                void set_parameters( int &amp;i, double wi, epdf* ep){w(i)=wi;Coms(i)=ep;}</span>
+<a name="l00412"></a>00412 <span class="comment">                vec mean(){vec pom; for(int i=0;i&lt;n;i++){pom+=Coms(i)-&gt;mean()*w(i);} return pom;};</span>
+<a name="l00413"></a>00413 <span class="comment">                vec sample() {it_error ( "Not implemented" );return 0;}</span>
+<a name="l00414"></a>00414 <span class="comment">        };</span>
+<a name="l00415"></a>00415 <span class="comment">        */</span>
+<a name="l00416"></a>00416 
+<a name="l00418"></a>00418 
+<a name="l00419"></a><a class="code" href="classbdm_1_1euni.html">00419</a>         <span class="keyword">class </span><a class="code" href="classbdm_1_1euni.html" title="Uniform distributed density on a rectangular support.">euni</a>: <span class="keyword">public</span> <a class="code" href="classbdm_1_1epdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a>
+<a name="l00420"></a>00420         {
+<a name="l00421"></a>00421                 <span class="keyword">protected</span>:
+<a name="l00423"></a><a class="code" href="classbdm_1_1euni.html#ff7ce6a2ef5ef0015bbd1398bed12f32">00423</a>                         vec <a class="code" href="classbdm_1_1euni.html#ff7ce6a2ef5ef0015bbd1398bed12f32" title="lower bound on support">low</a>;
+<a name="l00425"></a><a class="code" href="classbdm_1_1euni.html#cfad2dea4a62db6872bda8abd75f0de1">00425</a>                         vec <a class="code" href="classbdm_1_1euni.html#cfad2dea4a62db6872bda8abd75f0de1" title="upper bound on support">high</a>;
+<a name="l00427"></a><a class="code" href="classbdm_1_1euni.html#d3c27e331f90c754d80228108de8ed4c">00427</a>                         vec <a class="code" href="classbdm_1_1euni.html#d3c27e331f90c754d80228108de8ed4c" title="internal">distance</a>;
+<a name="l00429"></a><a class="code" href="classbdm_1_1euni.html#31bb13e8449a8eff35246d46dae35c20">00429</a>                         <span class="keywordtype">double</span> <a class="code" href="classbdm_1_1euni.html#31bb13e8449a8eff35246d46dae35c20" title="normalizing coefficients">nk</a>;
+<a name="l00431"></a><a class="code" href="classbdm_1_1euni.html#3e63be48dd58659663ca60cd18700476">00431</a>                         <span class="keywordtype">double</span> <a class="code" href="classbdm_1_1euni.html#3e63be48dd58659663ca60cd18700476" title="cache of log( nk )">lnk</a>;
+<a name="l00432"></a>00432                 <span class="keyword">public</span>:
+<a name="l00435"></a>00435                         <a class="code" href="classbdm_1_1euni.html" title="Uniform distributed density on a rectangular support.">euni</a> ( ) :<a class="code" href="classbdm_1_1epdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a> ( ) {}
+<a name="l00436"></a>00436                         <a class="code" href="classbdm_1_1euni.html" title="Uniform distributed density on a rectangular support.">euni</a> ( <span class="keyword">const</span> vec &amp;low0, <span class="keyword">const</span> vec &amp;high0 ) {set_parameters ( low0,high0 );}
+<a name="l00437"></a>00437                         <span class="keywordtype">void</span> set_parameters ( <span class="keyword">const</span> vec &amp;low0, <span class="keyword">const</span> vec &amp;high0 )
+<a name="l00438"></a>00438                         {
+<a name="l00439"></a>00439                                 <a class="code" href="classbdm_1_1euni.html#d3c27e331f90c754d80228108de8ed4c" title="internal">distance</a> = high0-low0;
+<a name="l00440"></a>00440                                 it_assert_debug ( min ( <a class="code" href="classbdm_1_1euni.html#d3c27e331f90c754d80228108de8ed4c" title="internal">distance</a> ) &gt;0.0,<span class="stringliteral">"bad support"</span> );
+<a name="l00441"></a>00441                                 <a class="code" href="classbdm_1_1euni.html#ff7ce6a2ef5ef0015bbd1398bed12f32" title="lower bound on support">low</a> = low0;
+<a name="l00442"></a>00442                                 <a class="code" href="classbdm_1_1euni.html#cfad2dea4a62db6872bda8abd75f0de1" title="upper bound on support">high</a> = high0;
+<a name="l00443"></a>00443                                 <a class="code" href="classbdm_1_1euni.html#31bb13e8449a8eff35246d46dae35c20" title="normalizing coefficients">nk</a> = prod ( 1.0/<a class="code" href="classbdm_1_1euni.html#d3c27e331f90c754d80228108de8ed4c" title="internal">distance</a> );
+<a name="l00444"></a>00444                                 <a class="code" href="classbdm_1_1euni.html#3e63be48dd58659663ca60cd18700476" title="cache of log( nk )">lnk</a> = log ( <a class="code" href="classbdm_1_1euni.html#31bb13e8449a8eff35246d46dae35c20" title="normalizing coefficients">nk</a> );
+<a name="l00445"></a>00445                                 <a class="code" href="classbdm_1_1epdf.html#16adac20ec7fe07e1ea0b27d917788ce" title="dimension of the random variable">dim</a> = <a class="code" href="classbdm_1_1euni.html#ff7ce6a2ef5ef0015bbd1398bed12f32" title="lower bound on support">low</a>.length();
+<a name="l00446"></a>00446                         }
+<a name="l00448"></a>00448 
+<a name="l00449"></a>00449                         <span class="keywordtype">double</span> eval ( <span class="keyword">const</span> vec &amp;val )<span class="keyword"> const  </span>{<span class="keywordflow">return</span> <a class="code" href="classbdm_1_1euni.html#31bb13e8449a8eff35246d46dae35c20" title="normalizing coefficients">nk</a>;}
+<a name="l00450"></a><a class="code" href="classbdm_1_1euni.html#caa07b8307bd793d5339d6583e0aba81">00450</a>                         <span class="keywordtype">double</span> <a class="code" href="classbdm_1_1euni.html#caa07b8307bd793d5339d6583e0aba81" title="Compute log-probability of argument val.">evallog</a> ( <span class="keyword">const</span> vec &amp;val )<span class="keyword"> const  </span>{<span class="keywordflow">return</span> <a class="code" href="classbdm_1_1euni.html#3e63be48dd58659663ca60cd18700476" title="cache of log( nk )">lnk</a>;}
+<a name="l00451"></a><a class="code" href="classbdm_1_1euni.html#fc5df80359ead2918384b2004ce67194">00451</a>                         vec <a class="code" href="classbdm_1_1euni.html#fc5df80359ead2918384b2004ce67194" title="Returns a sample,  from density .">sample</a>()<span class="keyword"> const</span>
+<a name="l00452"></a>00452 <span class="keyword">                        </span>{
+<a name="l00453"></a>00453                                 vec smp ( <a class="code" href="classbdm_1_1epdf.html#16adac20ec7fe07e1ea0b27d917788ce" title="dimension of the random variable">dim</a> );
+<a name="l00454"></a>00454 <span class="preprocessor">#pragma omp critical</span>
+<a name="l00455"></a>00455 <span class="preprocessor"></span>                                UniRNG.sample_vector ( <a class="code" href="classbdm_1_1epdf.html#16adac20ec7fe07e1ea0b27d917788ce" title="dimension of the random variable">dim</a> ,smp );
+<a name="l00456"></a>00456                                 <span class="keywordflow">return</span> <a class="code" href="classbdm_1_1euni.html#ff7ce6a2ef5ef0015bbd1398bed12f32" title="lower bound on support">low</a>+elem_mult ( <a class="code" href="classbdm_1_1euni.html#d3c27e331f90c754d80228108de8ed4c" title="internal">distance</a>,smp );
+<a name="l00457"></a>00457                         }
+<a name="l00459"></a><a class="code" href="classbdm_1_1euni.html#46caa8c13aba2e6228f964208918b226">00459</a>                         vec <a class="code" href="classbdm_1_1euni.html#46caa8c13aba2e6228f964208918b226" title="set values of low and high ">mean</a>()<span class="keyword"> const </span>{<span class="keywordflow">return</span> ( <a class="code" href="classbdm_1_1euni.html#cfad2dea4a62db6872bda8abd75f0de1" title="upper bound on support">high</a>-<a class="code" href="classbdm_1_1euni.html#ff7ce6a2ef5ef0015bbd1398bed12f32" title="lower bound on support">low</a> ) /2.0;}
+<a name="l00460"></a><a class="code" href="classbdm_1_1euni.html#951f932155111f6053c980f672b4c22c">00460</a>                         vec <a class="code" href="classbdm_1_1euni.html#951f932155111f6053c980f672b4c22c" title="return expected variance (not covariance!)">variance</a>()<span class="keyword"> const </span>{<span class="keywordflow">return</span> ( pow ( <a class="code" href="classbdm_1_1euni.html#cfad2dea4a62db6872bda8abd75f0de1" title="upper bound on support">high</a>,2 ) +pow ( <a class="code" href="classbdm_1_1euni.html#ff7ce6a2ef5ef0015bbd1398bed12f32" title="lower bound on support">low</a>,2 ) +elem_mult ( <a class="code" href="classbdm_1_1euni.html#cfad2dea4a62db6872bda8abd75f0de1" title="upper bound on support">high</a>,<a class="code" href="classbdm_1_1euni.html#ff7ce6a2ef5ef0015bbd1398bed12f32" title="lower bound on support">low</a> ) ) /3.0;}
+<a name="l00461"></a>00461         };
 <a name="l00462"></a>00462 
-<a name="l00468"></a>00468         <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt;
-<a name="l00469"></a><a class="code" href="classbdm_1_1mlnorm.html">00469</a>         <span class="keyword">class </span><a class="code" href="classbdm_1_1mlnorm.html" title="Normal distributed linear function with linear function of mean value;.">mlnorm</a> : <span class="keyword">public</span> <a class="code" href="classbdm_1_1mEF.html" title="Exponential family model.">mEF</a>
-<a name="l00470"></a>00470         {
-<a name="l00471"></a>00471                 <span class="keyword">protected</span>:
-<a name="l00473"></a><a class="code" href="classbdm_1_1mlnorm.html#150ad6acb223b0a0abeaf92346686dcd">00473</a>                         <a class="code" href="classbdm_1_1enorm.html" title="Gaussian density with positive definite (decomposed) covariance matrix.">enorm&lt;sq_T&gt;</a> <a class="code" href="classbdm_1_1epdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a>;
-<a name="l00474"></a>00474                         mat A;
-<a name="l00475"></a>00475                         vec mu_const;
-<a name="l00476"></a>00476                         vec&amp; _mu; <span class="comment">//cached epdf.mu;</span>
-<a name="l00477"></a>00477                 <span class="keyword">public</span>:
-<a name="l00480"></a>00480                         <a class="code" href="classbdm_1_1mlnorm.html" title="Normal distributed linear function with linear function of mean value;.">mlnorm</a> ( ) :<a class="code" href="classbdm_1_1mEF.html" title="Exponential family model.">mEF</a> (),<a class="code" href="classbdm_1_1epdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a> ( ),A ( ),_mu ( <a class="code" href="classbdm_1_1epdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a>._mu() ) {<a class="code" href="classbdm_1_1mpdf.html#5eea43c56d38e4441bfb30270db949c0" title="pointer to internal epdf">ep</a> =&amp;<a class="code" href="classbdm_1_1epdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a>; };
-<a name="l00481"></a>00481                         <a class="code" href="classbdm_1_1mlnorm.html" title="Normal distributed linear function with linear function of mean value;.">mlnorm</a> ( <span class="keyword">const</span>  mat &amp;A, <span class="keyword">const</span> vec &amp;mu0, <span class="keyword">const</span> sq_T &amp;R ) :<a class="code" href="classbdm_1_1epdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a> ( ),_mu ( <a class="code" href="classbdm_1_1epdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a>._mu() )
-<a name="l00482"></a>00482                         {
-<a name="l00483"></a>00483                                 <a class="code" href="classbdm_1_1mpdf.html#5eea43c56d38e4441bfb30270db949c0" title="pointer to internal epdf">ep</a> =&amp;<a class="code" href="classbdm_1_1epdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a>; <a class="code" href="classbdm_1_1mlnorm.html#5d18dec3167584338a4775c1d165d96f" title="Set A and R.">set_parameters</a> ( A,mu0,R );
-<a name="l00484"></a>00484                         };
-<a name="l00486"></a>00486                         <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1mlnorm.html#5d18dec3167584338a4775c1d165d96f" title="Set A and R.">set_parameters</a> ( <span class="keyword">const</span>  mat &amp;A, <span class="keyword">const</span> vec &amp;mu0, <span class="keyword">const</span> sq_T &amp;R );
-<a name="l00489"></a>00489                         <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1mlnorm.html#0dafc0196e7e07fd06dc6716e0e18bc2">condition</a> ( <span class="keyword">const</span> vec &amp;cond );
-<a name="l00490"></a>00490 
-<a name="l00492"></a><a class="code" href="classbdm_1_1mlnorm.html#56e587952f94fcac6cfc999eae6dbced">00492</a>                         vec&amp; <a class="code" href="classbdm_1_1mlnorm.html#56e587952f94fcac6cfc999eae6dbced" title="access function">_mu_const</a>() {<span class="keywordflow">return</span> mu_const;}
-<a name="l00494"></a><a class="code" href="classbdm_1_1mlnorm.html#262a2a486bff585f34bb6a5005b02614">00494</a>                         mat&amp; <a class="code" href="classbdm_1_1mlnorm.html#262a2a486bff585f34bb6a5005b02614" title="access function">_A</a>() {<span class="keywordflow">return</span> A;}
-<a name="l00496"></a><a class="code" href="classbdm_1_1mlnorm.html#78120ecd1c2b1d7e80124b4603504604">00496</a>                         mat <a class="code" href="classbdm_1_1mlnorm.html#78120ecd1c2b1d7e80124b4603504604" title="access function">_R</a>() {<span class="keywordflow">return</span> <a class="code" href="classbdm_1_1epdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a>._R().to_mat();}
-<a name="l00497"></a>00497 
-<a name="l00498"></a>00498                         <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_M&gt;
-<a name="l00499"></a>00499                         <span class="keyword">friend</span> std::ostream &amp;operator&lt;&lt; ( std::ostream &amp;os,  mlnorm&lt;sq_M&gt; &amp;ml );
-<a name="l00500"></a>00500         };
-<a name="l00501"></a>00501 
-<a name="l00503"></a>00503         <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt;
-<a name="l00504"></a><a class="code" href="classbdm_1_1mgnorm.html">00504</a>         <span class="keyword">class </span><a class="code" href="classbdm_1_1mgnorm.html" title="Mpdf with general function for mean value.">mgnorm</a> : <span class="keyword">public</span> <a class="code" href="classbdm_1_1mEF.html" title="Exponential family model.">mEF</a>
-<a name="l00505"></a>00505         {
-<a name="l00506"></a>00506                 <span class="keyword">protected</span>:
-<a name="l00508"></a><a class="code" href="classbdm_1_1mgnorm.html#8f7a376a1d2197e0634557e88e03104a">00508</a>                         <a class="code" href="classbdm_1_1enorm.html" title="Gaussian density with positive definite (decomposed) covariance matrix.">enorm&lt;sq_T&gt;</a> <a class="code" href="classbdm_1_1epdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a>;
-<a name="l00509"></a>00509                         vec &amp;mu;
-<a name="l00510"></a>00510                         <a class="code" href="classbdm_1_1fnc.html" title="Class representing function  of variable  represented by rv.">fnc</a>* g;
-<a name="l00511"></a>00511                 <span class="keyword">public</span>:
-<a name="l00513"></a><a class="code" href="classbdm_1_1mgnorm.html#1b014915d74470d3efab74e07cacb97d">00513</a>                         <a class="code" href="classbdm_1_1mgnorm.html#1b014915d74470d3efab74e07cacb97d" title="default constructor">mgnorm</a>() :mu ( <a class="code" href="classbdm_1_1epdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a>._mu() ) {<a class="code" href="classbdm_1_1mpdf.html#5eea43c56d38e4441bfb30270db949c0" title="pointer to internal epdf">ep</a>=&amp;<a class="code" href="classbdm_1_1epdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a>;}
-<a name="l00515"></a><a class="code" href="classbdm_1_1mgnorm.html#578e02458e2a0d17f3864826b6ebd564">00515</a>                         <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1mgnorm.html#578e02458e2a0d17f3864826b6ebd564" title="set mean function">set_parameters</a> ( <a class="code" href="classbdm_1_1fnc.html" title="Class representing function  of variable  represented by rv.">fnc</a>* g0, <span class="keyword">const</span> sq_T &amp;R0 ) {g=g0; <a class="code" href="classbdm_1_1epdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a>.set_parameters ( zeros ( g-&gt;<a class="code" href="classbdm_1_1fnc.html#083832294da9d1e40804158b979c4341" title="access function">dimension</a>() ), R0 );}
-<a name="l00516"></a><a class="code" href="classbdm_1_1mgnorm.html#b31d63472cf6a1030cd8dbd8094c1f6d">00516</a>                         <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1mgnorm.html#b31d63472cf6a1030cd8dbd8094c1f6d" title="Update ep so that it represents this mpdf conditioned on rvc = cond.">condition</a> ( <span class="keyword">const</span> vec &amp;cond ) {mu=g-&gt;<a class="code" href="classbdm_1_1fnc.html#6277b11d7fffc7ef8a2fa3e84ae5bad4" title="function evaluates numerical value of  at  cond ">eval</a> ( cond );};
-<a name="l00517"></a>00517 
+<a name="l00463"></a>00463 
+<a name="l00469"></a>00469         <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt;
+<a name="l00470"></a><a class="code" href="classbdm_1_1mlnorm.html">00470</a>         <span class="keyword">class </span><a class="code" href="classbdm_1_1mlnorm.html" title="Normal distributed linear function with linear function of mean value;.">mlnorm</a> : <span class="keyword">public</span> <a class="code" href="classbdm_1_1mEF.html" title="Exponential family model.">mEF</a>
+<a name="l00471"></a>00471         {
+<a name="l00472"></a>00472                 <span class="keyword">protected</span>:
+<a name="l00474"></a><a class="code" href="classbdm_1_1mlnorm.html#150ad6acb223b0a0abeaf92346686dcd">00474</a>                         <a class="code" href="classbdm_1_1enorm.html" title="Gaussian density with positive definite (decomposed) covariance matrix.">enorm&lt;sq_T&gt;</a> <a class="code" href="classbdm_1_1epdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a>;
+<a name="l00475"></a>00475                         mat A;
+<a name="l00476"></a>00476                         vec mu_const;
+<a name="l00477"></a>00477                         vec&amp; _mu; <span class="comment">//cached epdf.mu;</span>
+<a name="l00478"></a>00478                 <span class="keyword">public</span>:
+<a name="l00481"></a>00481                         <a class="code" href="classbdm_1_1mlnorm.html" title="Normal distributed linear function with linear function of mean value;.">mlnorm</a> ( ) :<a class="code" href="classbdm_1_1mEF.html" title="Exponential family model.">mEF</a> (),<a class="code" href="classbdm_1_1epdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a> ( ),A ( ),_mu ( <a class="code" href="classbdm_1_1epdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a>._mu() ) {<a class="code" href="classbdm_1_1mpdf.html#5eea43c56d38e4441bfb30270db949c0" title="pointer to internal epdf">ep</a> =&amp;<a class="code" href="classbdm_1_1epdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a>; };
+<a name="l00482"></a>00482                         <a class="code" href="classbdm_1_1mlnorm.html" title="Normal distributed linear function with linear function of mean value;.">mlnorm</a> ( <span class="keyword">const</span>  mat &amp;A, <span class="keyword">const</span> vec &amp;mu0, <span class="keyword">const</span> sq_T &amp;R ) :<a class="code" href="classbdm_1_1epdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a> ( ),_mu ( <a class="code" href="classbdm_1_1epdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a>._mu() )
+<a name="l00483"></a>00483                         {
+<a name="l00484"></a>00484                                 <a class="code" href="classbdm_1_1mpdf.html#5eea43c56d38e4441bfb30270db949c0" title="pointer to internal epdf">ep</a> =&amp;<a class="code" href="classbdm_1_1epdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a>; <a class="code" href="classbdm_1_1mlnorm.html#5d18dec3167584338a4775c1d165d96f" title="Set A and R.">set_parameters</a> ( A,mu0,R );
+<a name="l00485"></a>00485                         };
+<a name="l00487"></a>00487                         <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1mlnorm.html#5d18dec3167584338a4775c1d165d96f" title="Set A and R.">set_parameters</a> ( <span class="keyword">const</span>  mat &amp;A, <span class="keyword">const</span> vec &amp;mu0, <span class="keyword">const</span> sq_T &amp;R );
+<a name="l00490"></a>00490                         <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1mlnorm.html#0dafc0196e7e07fd06dc6716e0e18bc2">condition</a> ( <span class="keyword">const</span> vec &amp;cond );
+<a name="l00491"></a>00491 
+<a name="l00493"></a><a class="code" href="classbdm_1_1mlnorm.html#56e587952f94fcac6cfc999eae6dbced">00493</a>                         vec&amp; <a class="code" href="classbdm_1_1mlnorm.html#56e587952f94fcac6cfc999eae6dbced" title="access function">_mu_const</a>() {<span class="keywordflow">return</span> mu_const;}
+<a name="l00495"></a><a class="code" href="classbdm_1_1mlnorm.html#262a2a486bff585f34bb6a5005b02614">00495</a>                         mat&amp; <a class="code" href="classbdm_1_1mlnorm.html#262a2a486bff585f34bb6a5005b02614" title="access function">_A</a>() {<span class="keywordflow">return</span> A;}
+<a name="l00497"></a><a class="code" href="classbdm_1_1mlnorm.html#78120ecd1c2b1d7e80124b4603504604">00497</a>                         mat <a class="code" href="classbdm_1_1mlnorm.html#78120ecd1c2b1d7e80124b4603504604" title="access function">_R</a>() {<span class="keywordflow">return</span> <a class="code" href="classbdm_1_1epdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a>._R().to_mat();}
+<a name="l00498"></a>00498 
+<a name="l00499"></a>00499                         <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_M&gt;
+<a name="l00500"></a>00500                         <span class="keyword">friend</span> std::ostream &amp;operator&lt;&lt; ( std::ostream &amp;os,  mlnorm&lt;sq_M&gt; &amp;ml );
+<a name="l00501"></a>00501         };
+<a name="l00502"></a>00502 
+<a name="l00504"></a>00504         <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt;
+<a name="l00505"></a><a class="code" href="classbdm_1_1mgnorm.html">00505</a>         <span class="keyword">class </span><a class="code" href="classbdm_1_1mgnorm.html" title="Mpdf with general function for mean value.">mgnorm</a> : <span class="keyword">public</span> <a class="code" href="classbdm_1_1mEF.html" title="Exponential family model.">mEF</a>
+<a name="l00506"></a>00506         {
+<a name="l00507"></a>00507                 <span class="keyword">protected</span>:
+<a name="l00509"></a><a class="code" href="classbdm_1_1mgnorm.html#8f7a376a1d2197e0634557e88e03104a">00509</a>                         <a class="code" href="classbdm_1_1enorm.html" title="Gaussian density with positive definite (decomposed) covariance matrix.">enorm&lt;sq_T&gt;</a> <a class="code" href="classbdm_1_1epdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a>;
+<a name="l00510"></a>00510                         vec &amp;mu;
+<a name="l00511"></a>00511                         <a class="code" href="classbdm_1_1fnc.html" title="Class representing function  of variable  represented by rv.">fnc</a>* g;
+<a name="l00512"></a>00512                 <span class="keyword">public</span>:
+<a name="l00514"></a><a class="code" href="classbdm_1_1mgnorm.html#1b014915d74470d3efab74e07cacb97d">00514</a>                         <a class="code" href="classbdm_1_1mgnorm.html#1b014915d74470d3efab74e07cacb97d" title="default constructor">mgnorm</a>() :mu ( <a class="code" href="classbdm_1_1epdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a>._mu() ) {<a class="code" href="classbdm_1_1mpdf.html#5eea43c56d38e4441bfb30270db949c0" title="pointer to internal epdf">ep</a>=&amp;<a class="code" href="classbdm_1_1epdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a>;}
+<a name="l00516"></a><a class="code" href="classbdm_1_1mgnorm.html#578e02458e2a0d17f3864826b6ebd564">00516</a>                         <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1mgnorm.html#578e02458e2a0d17f3864826b6ebd564" title="set mean function">set_parameters</a> ( <a class="code" href="classbdm_1_1fnc.html" title="Class representing function  of variable  represented by rv.">fnc</a>* g0, <span class="keyword">const</span> sq_T &amp;R0 ) {g=g0; <a class="code" href="classbdm_1_1epdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a>.set_parameters ( zeros ( g-&gt;<a class="code" href="classbdm_1_1fnc.html#083832294da9d1e40804158b979c4341" title="access function">dimension</a>() ), R0 );}
+<a name="l00517"></a><a class="code" href="classbdm_1_1mgnorm.html#b31d63472cf6a1030cd8dbd8094c1f6d">00517</a>                         <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1mgnorm.html#b31d63472cf6a1030cd8dbd8094c1f6d" title="Update ep so that it represents this mpdf conditioned on rvc = cond.">condition</a> ( <span class="keyword">const</span> vec &amp;cond ) {mu=g-&gt;<a class="code" href="classbdm_1_1fnc.html#6277b11d7fffc7ef8a2fa3e84ae5bad4" title="function evaluates numerical value of  at  cond ">eval</a> ( cond );};
 <a name="l00518"></a>00518 
-<a name="l00546"></a><a class="code" href="classbdm_1_1mgnorm.html#d23d2c9b147c01785d5a4239c118ddf1">00546</a>                         <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1mgnorm.html#d23d2c9b147c01785d5a4239c118ddf1">from_setting</a>( <span class="keyword">const</span> Setting &amp;root ) 
-<a name="l00547"></a>00547                         {       
-<a name="l00548"></a>00548                                 <a class="code" href="classbdm_1_1fnc.html" title="Class representing function  of variable  represented by rv.">fnc</a>* g = UI::build&lt;fnc&gt;( root, <span class="stringliteral">"g"</span> );
-<a name="l00549"></a>00549 
-<a name="l00550"></a>00550                                 mat R;
-<a name="l00551"></a>00551                                 <span class="keywordflow">if</span> ( root.exists( <span class="stringliteral">"dR"</span> ) )
-<a name="l00552"></a>00552                                 {
-<a name="l00553"></a>00553                                         vec dR;
-<a name="l00554"></a>00554                                         <a class="code" href="classbdm_1_1UI.html#652bfd23f5052e4f1cb317057d74a3e2" title="This methods tries to build a new double matrix.">UI::get</a>( dR, root, <span class="stringliteral">"dR"</span> );
-<a name="l00555"></a>00555                                         R=diag(dR);
-<a name="l00556"></a>00556                                 }
-<a name="l00557"></a>00557                                 <span class="keywordflow">else</span> 
-<a name="l00558"></a>00558                                         <a class="code" href="classbdm_1_1UI.html#652bfd23f5052e4f1cb317057d74a3e2" title="This methods tries to build a new double matrix.">UI::get</a>( R, root, <span class="stringliteral">"R"</span>);                                 
-<a name="l00559"></a>00559                 
-<a name="l00560"></a>00560                                 <a class="code" href="classbdm_1_1mgnorm.html#578e02458e2a0d17f3864826b6ebd564" title="set mean function">set_parameters</a>(g,R);
-<a name="l00561"></a>00561                         }
-<a name="l00562"></a>00562 
-<a name="l00563"></a>00563                         <span class="comment">/*void mgnorm::to_setting( Setting &amp;root ) const</span>
-<a name="l00564"></a>00564 <span class="comment">                        {       </span>
-<a name="l00565"></a>00565 <span class="comment">                                Transport::to_setting( root );</span>
-<a name="l00566"></a>00566 <span class="comment"></span>
-<a name="l00567"></a>00567 <span class="comment">                                Setting &amp;kilometers_setting = root.add("kilometers", Setting::TypeInt );</span>
-<a name="l00568"></a>00568 <span class="comment">                                kilometers_setting = kilometers;</span>
-<a name="l00569"></a>00569 <span class="comment"></span>
-<a name="l00570"></a>00570 <span class="comment">                                UI::save( passengers, root, "passengers" );</span>
-<a name="l00571"></a>00571 <span class="comment">                        }*/</span>
-<a name="l00572"></a>00572 
-<a name="l00573"></a>00573         };
-<a name="l00574"></a>00574 
-<a name="l00575"></a>00575         UIREGISTER(mgnorm&lt;chmat&gt;);
-<a name="l00576"></a>00576 
+<a name="l00519"></a>00519 
+<a name="l00547"></a><a class="code" href="classbdm_1_1mgnorm.html#d23d2c9b147c01785d5a4239c118ddf1">00547</a>                         <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1mgnorm.html#d23d2c9b147c01785d5a4239c118ddf1">from_setting</a>( <span class="keyword">const</span> Setting &amp;root ) 
+<a name="l00548"></a>00548                         {       
+<a name="l00549"></a>00549                                 <a class="code" href="classbdm_1_1fnc.html" title="Class representing function  of variable  represented by rv.">fnc</a>* g = UI::build&lt;fnc&gt;( root, <span class="stringliteral">"g"</span> );
+<a name="l00550"></a>00550 
+<a name="l00551"></a>00551                                 mat R;
+<a name="l00552"></a>00552                                 <span class="keywordflow">if</span> ( root.exists( <span class="stringliteral">"dR"</span> ) )
+<a name="l00553"></a>00553                                 {
+<a name="l00554"></a>00554                                         vec dR;
+<a name="l00555"></a>00555                                         <a class="code" href="classbdm_1_1UI.html#652bfd23f5052e4f1cb317057d74a3e2" title="This methods tries to build a new double matrix.">UI::get</a>( dR, root, <span class="stringliteral">"dR"</span> );
+<a name="l00556"></a>00556                                         R=diag(dR);
+<a name="l00557"></a>00557                                 }
+<a name="l00558"></a>00558                                 <span class="keywordflow">else</span> 
+<a name="l00559"></a>00559                                         <a class="code" href="classbdm_1_1UI.html#652bfd23f5052e4f1cb317057d74a3e2" title="This methods tries to build a new double matrix.">UI::get</a>( R, root, <span class="stringliteral">"R"</span>);                                 
+<a name="l00560"></a>00560                 
+<a name="l00561"></a>00561                                 <a class="code" href="classbdm_1_1mgnorm.html#578e02458e2a0d17f3864826b6ebd564" title="set mean function">set_parameters</a>(g,R);
+<a name="l00562"></a>00562                         }
+<a name="l00563"></a>00563 
+<a name="l00564"></a>00564                         <span class="comment">/*void mgnorm::to_setting( Setting &amp;root ) const</span>
+<a name="l00565"></a>00565 <span class="comment">                        {       </span>
+<a name="l00566"></a>00566 <span class="comment">                                Transport::to_setting( root );</span>
+<a name="l00567"></a>00567 <span class="comment"></span>
+<a name="l00568"></a>00568 <span class="comment">                                Setting &amp;kilometers_setting = root.add("kilometers", Setting::TypeInt );</span>
+<a name="l00569"></a>00569 <span class="comment">                                kilometers_setting = kilometers;</span>
+<a name="l00570"></a>00570 <span class="comment"></span>
+<a name="l00571"></a>00571 <span class="comment">                                UI::save( passengers, root, "passengers" );</span>
+<a name="l00572"></a>00572 <span class="comment">                        }*/</span>
+<a name="l00573"></a>00573 
+<a name="l00574"></a>00574         };
+<a name="l00575"></a>00575 
+<a name="l00576"></a>00576         UIREGISTER(mgnorm&lt;chmat&gt;);
 <a name="l00577"></a>00577 
-<a name="l00585"></a><a class="code" href="classbdm_1_1mlstudent.html">00585</a>         <span class="keyword">class </span><a class="code" href="classbdm_1_1mlstudent.html">mlstudent</a> : <span class="keyword">public</span> <a class="code" href="classbdm_1_1mlnorm.html" title="Normal distributed linear function with linear function of mean value;.">mlnorm</a>&lt;ldmat&gt;
-<a name="l00586"></a>00586         {
-<a name="l00587"></a>00587                 <span class="keyword">protected</span>:
-<a name="l00588"></a>00588                         <a class="code" href="classldmat.html" title="Matrix stored in LD form, (commonly known as UD).">ldmat</a> Lambda;
-<a name="l00589"></a>00589                         <a class="code" href="classldmat.html" title="Matrix stored in LD form, (commonly known as UD).">ldmat</a> &amp;<a class="code" href="classbdm_1_1mlnorm.html#78120ecd1c2b1d7e80124b4603504604" title="access function">_R</a>;
-<a name="l00590"></a>00590                         <a class="code" href="classldmat.html" title="Matrix stored in LD form, (commonly known as UD).">ldmat</a> Re;
-<a name="l00591"></a>00591                 <span class="keyword">public</span>:
-<a name="l00592"></a>00592                         <a class="code" href="classbdm_1_1mlstudent.html">mlstudent</a> ( ) :<a class="code" href="classbdm_1_1mlnorm.html">mlnorm&lt;ldmat&gt;</a> (),
-<a name="l00593"></a>00593                                         Lambda (),      <a class="code" href="classbdm_1_1mlnorm.html#78120ecd1c2b1d7e80124b4603504604" title="access function">_R</a> ( <a class="code" href="classbdm_1_1epdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a>._R() ) {}
-<a name="l00594"></a>00594                         <span class="keywordtype">void</span> set_parameters ( <span class="keyword">const</span> mat &amp;A0, <span class="keyword">const</span> vec &amp;mu0, <span class="keyword">const</span> <a class="code" href="classldmat.html" title="Matrix stored in LD form, (commonly known as UD).">ldmat</a> &amp;R0, <span class="keyword">const</span> <a class="code" href="classldmat.html" title="Matrix stored in LD form, (commonly known as UD).">ldmat</a>&amp; Lambda0 )
-<a name="l00595"></a>00595                         {
-<a name="l00596"></a>00596                                 it_assert_debug ( A0.rows() ==mu0.length(),<span class="stringliteral">""</span> );
-<a name="l00597"></a>00597                                 it_assert_debug ( R0.<a class="code" href="group__math.html#g96dfb21865db4f5bd36fa70f9b0b1163" title="access function">rows</a>() ==A0.rows(),<span class="stringliteral">""</span> );
-<a name="l00598"></a>00598 
-<a name="l00599"></a>00599                                 <a class="code" href="classbdm_1_1epdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a>.set_parameters ( mu0,Lambda ); <span class="comment">//</span>
-<a name="l00600"></a>00600                                 A = A0;
-<a name="l00601"></a>00601                                 mu_const = mu0;
-<a name="l00602"></a>00602                                 Re=R0;
-<a name="l00603"></a>00603                                 Lambda = Lambda0;
-<a name="l00604"></a>00604                         }
-<a name="l00605"></a><a class="code" href="classbdm_1_1mlstudent.html#efd37560585c8613897f30d3c2f58d0d">00605</a>                         <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1mlstudent.html#efd37560585c8613897f30d3c2f58d0d">condition</a> ( <span class="keyword">const</span> vec &amp;cond )
-<a name="l00606"></a>00606                         {
-<a name="l00607"></a>00607                                 _mu = A*cond + mu_const;
-<a name="l00608"></a>00608                                 <span class="keywordtype">double</span> zeta;
-<a name="l00609"></a>00609                                 <span class="comment">//ugly hack!</span>
-<a name="l00610"></a>00610                                 <span class="keywordflow">if</span> ( ( cond.length() +1 ) ==Lambda.<a class="code" href="group__math.html#g96dfb21865db4f5bd36fa70f9b0b1163" title="access function">rows</a>() )
-<a name="l00611"></a>00611                                 {
-<a name="l00612"></a>00612                                         zeta = Lambda.<a class="code" href="classldmat.html#d876c5f83e02b3e809b35c9de5068f14" title="Evaluates quadratic form ;.">invqform</a> ( concat ( cond, vec_1 ( 1.0 ) ) );
-<a name="l00613"></a>00613                                 }
-<a name="l00614"></a>00614                                 <span class="keywordflow">else</span>
-<a name="l00615"></a>00615                                 {
-<a name="l00616"></a>00616                                         zeta = Lambda.<a class="code" href="classldmat.html#d876c5f83e02b3e809b35c9de5068f14" title="Evaluates quadratic form ;.">invqform</a> ( cond );
-<a name="l00617"></a>00617                                 }
-<a name="l00618"></a>00618                                 <a class="code" href="classbdm_1_1mlnorm.html#78120ecd1c2b1d7e80124b4603504604" title="access function">_R</a> = Re;
-<a name="l00619"></a>00619                                 <a class="code" href="classbdm_1_1mlnorm.html#78120ecd1c2b1d7e80124b4603504604" title="access function">_R</a>*= ( 1+zeta );<span class="comment">// / ( nu ); &lt;&lt; nu is in Re!!!!!!</span>
-<a name="l00620"></a>00620                         };
-<a name="l00621"></a>00621 
-<a name="l00622"></a>00622         };
-<a name="l00632"></a><a class="code" href="classbdm_1_1mgamma.html">00632</a>         <span class="keyword">class </span><a class="code" href="classbdm_1_1mgamma.html" title="Gamma random walk.">mgamma</a> : <span class="keyword">public</span> <a class="code" href="classbdm_1_1mEF.html" title="Exponential family model.">mEF</a>
-<a name="l00633"></a>00633         {
-<a name="l00634"></a>00634                 <span class="keyword">protected</span>:
-<a name="l00636"></a><a class="code" href="classbdm_1_1mgamma.html#bdc9f1e9e03c09e91103fee269864438">00636</a>                         <a class="code" href="classbdm_1_1egamma.html" title="Gamma posterior density.">egamma</a> <a class="code" href="classbdm_1_1epdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a>;
-<a name="l00638"></a><a class="code" href="classbdm_1_1mgamma.html#b20cf88cca1fe9b0b8f2a412608bfd09">00638</a>                         <span class="keywordtype">double</span> <a class="code" href="classbdm_1_1mgamma.html#b20cf88cca1fe9b0b8f2a412608bfd09" title="Constant .">k</a>;
-<a name="l00640"></a><a class="code" href="classbdm_1_1mgamma.html#3d95f4dde9214ff6dba265e18af60312">00640</a>                         vec &amp;<a class="code" href="classbdm_1_1mgamma.html#3d95f4dde9214ff6dba265e18af60312" title="cache of epdf.beta">_beta</a>;
-<a name="l00641"></a>00641 
-<a name="l00642"></a>00642                 <span class="keyword">public</span>:
-<a name="l00644"></a><a class="code" href="classbdm_1_1mgamma.html#1a9dc8661e5b214a8185d6e6b9956eb1">00644</a>                         <a class="code" href="classbdm_1_1mgamma.html#1a9dc8661e5b214a8185d6e6b9956eb1" title="Constructor.">mgamma</a> ( ) : <a class="code" href="classbdm_1_1mEF.html" title="Exponential family model.">mEF</a> ( ), <a class="code" href="classbdm_1_1epdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a> (), <a class="code" href="classbdm_1_1mgamma.html#3d95f4dde9214ff6dba265e18af60312" title="cache of epdf.beta">_beta</a> ( <a class="code" href="classbdm_1_1epdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a>.<a class="code" href="classbdm_1_1mgamma.html#3d95f4dde9214ff6dba265e18af60312" title="cache of epdf.beta">_beta</a>() ) {<a class="code" href="classbdm_1_1mpdf.html#5eea43c56d38e4441bfb30270db949c0" title="pointer to internal epdf">ep</a>=&amp;<a class="code" href="classbdm_1_1epdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a>;};
-<a name="l00646"></a>00646                         <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1mgamma.html#a0f21c2557b233a85838b497d040ab14" title="Set value of k.">set_parameters</a> ( <span class="keywordtype">double</span> <a class="code" href="classbdm_1_1mgamma.html#b20cf88cca1fe9b0b8f2a412608bfd09" title="Constant .">k</a>, <span class="keyword">const</span> vec &amp;beta0 );
-<a name="l00647"></a><a class="code" href="classbdm_1_1mgamma.html#8996500f1885e39cde30221b20900bff">00647</a>                         <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1mgamma.html#8996500f1885e39cde30221b20900bff" title="Update ep so that it represents this mpdf conditioned on rvc = cond.">condition</a> ( <span class="keyword">const</span> vec &amp;val ) {<a class="code" href="classbdm_1_1mgamma.html#3d95f4dde9214ff6dba265e18af60312" title="cache of epdf.beta">_beta</a>=<a class="code" href="classbdm_1_1mgamma.html#b20cf88cca1fe9b0b8f2a412608bfd09" title="Constant .">k</a>/val;};
-<a name="l00648"></a>00648         };
-<a name="l00649"></a>00649 
-<a name="l00659"></a><a class="code" href="classbdm_1_1migamma.html">00659</a>         <span class="keyword">class </span><a class="code" href="classbdm_1_1migamma.html" title="Inverse-Gamma random walk.">migamma</a> : <span class="keyword">public</span> <a class="code" href="classbdm_1_1mEF.html" title="Exponential family model.">mEF</a>
-<a name="l00660"></a>00660         {
-<a name="l00661"></a>00661                 <span class="keyword">protected</span>:
-<a name="l00663"></a><a class="code" href="classbdm_1_1migamma.html#a31b39d4179551b593c9e0d7d756783a">00663</a>                         <a class="code" href="classbdm_1_1eigamma.html" title="Inverse-Gamma posterior density.">eigamma</a> <a class="code" href="classbdm_1_1epdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a>;
-<a name="l00665"></a><a class="code" href="classbdm_1_1migamma.html#dc56bc9da542e0103ec16b9be8e5e38c">00665</a>                         <span class="keywordtype">double</span> <a class="code" href="classbdm_1_1migamma.html#dc56bc9da542e0103ec16b9be8e5e38c" title="Constant .">k</a>;
-<a name="l00667"></a><a class="code" href="classbdm_1_1migamma.html#c9847093da59a9ba0ebb68d2c592f5dc">00667</a>                         vec &amp;<a class="code" href="classbdm_1_1migamma.html#c9847093da59a9ba0ebb68d2c592f5dc" title="cache of epdf.alpha">_alpha</a>;
-<a name="l00669"></a><a class="code" href="classbdm_1_1migamma.html#0d854c047001b5465cf1ba21f52904b5">00669</a>                         vec &amp;<a class="code" href="classbdm_1_1migamma.html#0d854c047001b5465cf1ba21f52904b5" title="cache of epdf.beta">_beta</a>;
-<a name="l00670"></a>00670 
-<a name="l00671"></a>00671                 <span class="keyword">public</span>:
-<a name="l00674"></a>00674                         <a class="code" href="classbdm_1_1migamma.html" title="Inverse-Gamma random walk.">migamma</a> ( ) : <a class="code" href="classbdm_1_1mEF.html" title="Exponential family model.">mEF</a> (), <a class="code" href="classbdm_1_1epdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a> ( ), <a class="code" href="classbdm_1_1migamma.html#c9847093da59a9ba0ebb68d2c592f5dc" title="cache of epdf.alpha">_alpha</a> ( <a class="code" href="classbdm_1_1epdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a>.<a class="code" href="classbdm_1_1migamma.html#c9847093da59a9ba0ebb68d2c592f5dc" title="cache of epdf.alpha">_alpha</a>() ), <a class="code" href="classbdm_1_1migamma.html#0d854c047001b5465cf1ba21f52904b5" title="cache of epdf.beta">_beta</a> ( <a class="code" href="classbdm_1_1epdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a>.<a class="code" href="classbdm_1_1migamma.html#0d854c047001b5465cf1ba21f52904b5" title="cache of epdf.beta">_beta</a>() ) {<a class="code" href="classbdm_1_1mpdf.html#5eea43c56d38e4441bfb30270db949c0" title="pointer to internal epdf">ep</a>=&amp;<a class="code" href="classbdm_1_1epdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a>;};
-<a name="l00675"></a>00675                         <a class="code" href="classbdm_1_1migamma.html" title="Inverse-Gamma random walk.">migamma</a> ( <span class="keyword">const</span> <a class="code" href="classbdm_1_1migamma.html" title="Inverse-Gamma random walk.">migamma</a> &amp;m ) : <a class="code" href="classbdm_1_1mEF.html" title="Exponential family model.">mEF</a> (), <a class="code" href="classbdm_1_1epdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a> ( m.<a class="code" href="classbdm_1_1epdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a> ), <a class="code" href="classbdm_1_1migamma.html#c9847093da59a9ba0ebb68d2c592f5dc" title="cache of epdf.alpha">_alpha</a> ( <a class="code" href="classbdm_1_1epdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a>.<a class="code" href="classbdm_1_1migamma.html#c9847093da59a9ba0ebb68d2c592f5dc" title="cache of epdf.alpha">_alpha</a>() ), <a class="code" href="classbdm_1_1migamma.html#0d854c047001b5465cf1ba21f52904b5" title="cache of epdf.beta">_beta</a> ( <a class="code" href="classbdm_1_1epdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a>.<a class="code" href="classbdm_1_1migamma.html#0d854c047001b5465cf1ba21f52904b5" title="cache of epdf.beta">_beta</a>() ) {<a class="code" href="classbdm_1_1mpdf.html#5eea43c56d38e4441bfb30270db949c0" title="pointer to internal epdf">ep</a>=&amp;<a class="code" href="classbdm_1_1epdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a>;};
-<a name="l00677"></a>00677 
-<a name="l00679"></a><a class="code" href="classbdm_1_1migamma.html#8b10ab922e2a7bae2fb6bb3efc7b6151">00679</a>                         <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1migamma.html#8b10ab922e2a7bae2fb6bb3efc7b6151" title="Set value of k.">set_parameters</a> ( <span class="keywordtype">int</span> len, <span class="keywordtype">double</span> k0 )
-<a name="l00680"></a>00680                         {
-<a name="l00681"></a>00681                                 <a class="code" href="classbdm_1_1migamma.html#dc56bc9da542e0103ec16b9be8e5e38c" title="Constant .">k</a>=k0;
-<a name="l00682"></a>00682                                 <a class="code" href="classbdm_1_1epdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a>.set_parameters ( ( 1.0/ ( <a class="code" href="classbdm_1_1migamma.html#dc56bc9da542e0103ec16b9be8e5e38c" title="Constant .">k</a>*<a class="code" href="classbdm_1_1migamma.html#dc56bc9da542e0103ec16b9be8e5e38c" title="Constant .">k</a> ) +2.0 ) *ones ( len ) <span class="comment">/*alpha*/</span>, ones ( len ) <span class="comment">/*beta*/</span> );
-<a name="l00683"></a>00683                                 <a class="code" href="classbdm_1_1mpdf.html#7c1900976ff13dbc09c9729b3bbff9e6" title="dimension of the condition">dimc</a> = dimension();
-<a name="l00684"></a>00684                         };
-<a name="l00685"></a><a class="code" href="classbdm_1_1migamma.html#7a34b1e2e3aa2250d7c0ed7df1665b8c">00685</a>                         <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1migamma.html#7a34b1e2e3aa2250d7c0ed7df1665b8c" title="Update ep so that it represents this mpdf conditioned on rvc = cond.">condition</a> ( <span class="keyword">const</span> vec &amp;val )
-<a name="l00686"></a>00686                         {
-<a name="l00687"></a>00687                                 <a class="code" href="classbdm_1_1migamma.html#0d854c047001b5465cf1ba21f52904b5" title="cache of epdf.beta">_beta</a>=elem_mult ( val, ( <a class="code" href="classbdm_1_1migamma.html#c9847093da59a9ba0ebb68d2c592f5dc" title="cache of epdf.alpha">_alpha</a>-1.0 ) );
-<a name="l00688"></a>00688                         };
-<a name="l00689"></a>00689         };
-<a name="l00690"></a>00690 
+<a name="l00578"></a>00578 
+<a name="l00586"></a><a class="code" href="classbdm_1_1mlstudent.html">00586</a>         <span class="keyword">class </span><a class="code" href="classbdm_1_1mlstudent.html">mlstudent</a> : <span class="keyword">public</span> <a class="code" href="classbdm_1_1mlnorm.html" title="Normal distributed linear function with linear function of mean value;.">mlnorm</a>&lt;ldmat&gt;
+<a name="l00587"></a>00587         {
+<a name="l00588"></a>00588                 <span class="keyword">protected</span>:
+<a name="l00589"></a>00589                         <a class="code" href="classldmat.html" title="Matrix stored in LD form, (commonly known as UD).">ldmat</a> Lambda;
+<a name="l00590"></a>00590                         <a class="code" href="classldmat.html" title="Matrix stored in LD form, (commonly known as UD).">ldmat</a> &amp;<a class="code" href="classbdm_1_1mlnorm.html#78120ecd1c2b1d7e80124b4603504604" title="access function">_R</a>;
+<a name="l00591"></a>00591                         <a class="code" href="classldmat.html" title="Matrix stored in LD form, (commonly known as UD).">ldmat</a> Re;
+<a name="l00592"></a>00592                 <span class="keyword">public</span>:
+<a name="l00593"></a>00593                         <a class="code" href="classbdm_1_1mlstudent.html">mlstudent</a> ( ) :<a class="code" href="classbdm_1_1mlnorm.html">mlnorm&lt;ldmat&gt;</a> (),
+<a name="l00594"></a>00594                                         Lambda (),      <a class="code" href="classbdm_1_1mlnorm.html#78120ecd1c2b1d7e80124b4603504604" title="access function">_R</a> ( <a class="code" href="classbdm_1_1epdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a>._R() ) {}
+<a name="l00595"></a>00595                         <span class="keywordtype">void</span> set_parameters ( <span class="keyword">const</span> mat &amp;A0, <span class="keyword">const</span> vec &amp;mu0, <span class="keyword">const</span> <a class="code" href="classldmat.html" title="Matrix stored in LD form, (commonly known as UD).">ldmat</a> &amp;R0, <span class="keyword">const</span> <a class="code" href="classldmat.html" title="Matrix stored in LD form, (commonly known as UD).">ldmat</a>&amp; Lambda0 )
+<a name="l00596"></a>00596                         {
+<a name="l00597"></a>00597                                 it_assert_debug ( A0.rows() ==mu0.length(),<span class="stringliteral">""</span> );
+<a name="l00598"></a>00598                                 it_assert_debug ( R0.<a class="code" href="group__math.html#g96dfb21865db4f5bd36fa70f9b0b1163" title="access function">rows</a>() ==A0.rows(),<span class="stringliteral">""</span> );
+<a name="l00599"></a>00599 
+<a name="l00600"></a>00600                                 <a class="code" href="classbdm_1_1epdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a>.set_parameters ( mu0,Lambda ); <span class="comment">//</span>
+<a name="l00601"></a>00601                                 A = A0;
+<a name="l00602"></a>00602                                 mu_const = mu0;
+<a name="l00603"></a>00603                                 Re=R0;
+<a name="l00604"></a>00604                                 Lambda = Lambda0;
+<a name="l00605"></a>00605                         }
+<a name="l00606"></a><a class="code" href="classbdm_1_1mlstudent.html#efd37560585c8613897f30d3c2f58d0d">00606</a>                         <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1mlstudent.html#efd37560585c8613897f30d3c2f58d0d">condition</a> ( <span class="keyword">const</span> vec &amp;cond )
+<a name="l00607"></a>00607                         {
+<a name="l00608"></a>00608                                 _mu = A*cond + mu_const;
+<a name="l00609"></a>00609                                 <span class="keywordtype">double</span> zeta;
+<a name="l00610"></a>00610                                 <span class="comment">//ugly hack!</span>
+<a name="l00611"></a>00611                                 <span class="keywordflow">if</span> ( ( cond.length() +1 ) ==Lambda.<a class="code" href="group__math.html#g96dfb21865db4f5bd36fa70f9b0b1163" title="access function">rows</a>() )
+<a name="l00612"></a>00612                                 {
+<a name="l00613"></a>00613                                         zeta = Lambda.<a class="code" href="classldmat.html#d876c5f83e02b3e809b35c9de5068f14" title="Evaluates quadratic form ;.">invqform</a> ( concat ( cond, vec_1 ( 1.0 ) ) );
+<a name="l00614"></a>00614                                 }
+<a name="l00615"></a>00615                                 <span class="keywordflow">else</span>
+<a name="l00616"></a>00616                                 {
+<a name="l00617"></a>00617                                         zeta = Lambda.<a class="code" href="classldmat.html#d876c5f83e02b3e809b35c9de5068f14" title="Evaluates quadratic form ;.">invqform</a> ( cond );
+<a name="l00618"></a>00618                                 }
+<a name="l00619"></a>00619                                 <a class="code" href="classbdm_1_1mlnorm.html#78120ecd1c2b1d7e80124b4603504604" title="access function">_R</a> = Re;
+<a name="l00620"></a>00620                                 <a class="code" href="classbdm_1_1mlnorm.html#78120ecd1c2b1d7e80124b4603504604" title="access function">_R</a>*= ( 1+zeta );<span class="comment">// / ( nu ); &lt;&lt; nu is in Re!!!!!!</span>
+<a name="l00621"></a>00621                         };
+<a name="l00622"></a>00622 
+<a name="l00623"></a>00623         };
+<a name="l00633"></a><a class="code" href="classbdm_1_1mgamma.html">00633</a>         <span class="keyword">class </span><a class="code" href="classbdm_1_1mgamma.html" title="Gamma random walk.">mgamma</a> : <span class="keyword">public</span> <a class="code" href="classbdm_1_1mEF.html" title="Exponential family model.">mEF</a>
+<a name="l00634"></a>00634         {
+<a name="l00635"></a>00635                 <span class="keyword">protected</span>:
+<a name="l00637"></a><a class="code" href="classbdm_1_1mgamma.html#bdc9f1e9e03c09e91103fee269864438">00637</a>                         <a class="code" href="classbdm_1_1egamma.html" title="Gamma posterior density.">egamma</a> <a class="code" href="classbdm_1_1epdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a>;
+<a name="l00639"></a><a class="code" href="classbdm_1_1mgamma.html#b20cf88cca1fe9b0b8f2a412608bfd09">00639</a>                         <span class="keywordtype">double</span> <a class="code" href="classbdm_1_1mgamma.html#b20cf88cca1fe9b0b8f2a412608bfd09" title="Constant .">k</a>;
+<a name="l00641"></a><a class="code" href="classbdm_1_1mgamma.html#3d95f4dde9214ff6dba265e18af60312">00641</a>                         vec &amp;<a class="code" href="classbdm_1_1mgamma.html#3d95f4dde9214ff6dba265e18af60312" title="cache of epdf.beta">_beta</a>;
+<a name="l00642"></a>00642 
+<a name="l00643"></a>00643                 <span class="keyword">public</span>:
+<a name="l00645"></a><a class="code" href="classbdm_1_1mgamma.html#1a9dc8661e5b214a8185d6e6b9956eb1">00645</a>                         <a class="code" href="classbdm_1_1mgamma.html#1a9dc8661e5b214a8185d6e6b9956eb1" title="Constructor.">mgamma</a> ( ) : <a class="code" href="classbdm_1_1mEF.html" title="Exponential family model.">mEF</a> ( ), <a class="code" href="classbdm_1_1epdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a> (), <a class="code" href="classbdm_1_1mgamma.html#3d95f4dde9214ff6dba265e18af60312" title="cache of epdf.beta">_beta</a> ( <a class="code" href="classbdm_1_1epdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a>.<a class="code" href="classbdm_1_1mgamma.html#3d95f4dde9214ff6dba265e18af60312" title="cache of epdf.beta">_beta</a>() ) {<a class="code" href="classbdm_1_1mpdf.html#5eea43c56d38e4441bfb30270db949c0" title="pointer to internal epdf">ep</a>=&amp;<a class="code" href="classbdm_1_1epdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a>;};
+<a name="l00647"></a>00647                         <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1mgamma.html#a0f21c2557b233a85838b497d040ab14" title="Set value of k.">set_parameters</a> ( <span class="keywordtype">double</span> <a class="code" href="classbdm_1_1mgamma.html#b20cf88cca1fe9b0b8f2a412608bfd09" title="Constant .">k</a>, <span class="keyword">const</span> vec &amp;beta0 );
+<a name="l00648"></a><a class="code" href="classbdm_1_1mgamma.html#8996500f1885e39cde30221b20900bff">00648</a>                         <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1mgamma.html#8996500f1885e39cde30221b20900bff" title="Update ep so that it represents this mpdf conditioned on rvc = cond.">condition</a> ( <span class="keyword">const</span> vec &amp;val ) {<a class="code" href="classbdm_1_1mgamma.html#3d95f4dde9214ff6dba265e18af60312" title="cache of epdf.beta">_beta</a>=<a class="code" href="classbdm_1_1mgamma.html#b20cf88cca1fe9b0b8f2a412608bfd09" title="Constant .">k</a>/val;};
+<a name="l00649"></a>00649         };
+<a name="l00650"></a>00650 
+<a name="l00660"></a><a class="code" href="classbdm_1_1migamma.html">00660</a>         <span class="keyword">class </span><a class="code" href="classbdm_1_1migamma.html" title="Inverse-Gamma random walk.">migamma</a> : <span class="keyword">public</span> <a class="code" href="classbdm_1_1mEF.html" title="Exponential family model.">mEF</a>
+<a name="l00661"></a>00661         {
+<a name="l00662"></a>00662                 <span class="keyword">protected</span>:
+<a name="l00664"></a><a class="code" href="classbdm_1_1migamma.html#a31b39d4179551b593c9e0d7d756783a">00664</a>                         <a class="code" href="classbdm_1_1eigamma.html" title="Inverse-Gamma posterior density.">eigamma</a> <a class="code" href="classbdm_1_1epdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a>;
+<a name="l00666"></a><a class="code" href="classbdm_1_1migamma.html#dc56bc9da542e0103ec16b9be8e5e38c">00666</a>                         <span class="keywordtype">double</span> <a class="code" href="classbdm_1_1migamma.html#dc56bc9da542e0103ec16b9be8e5e38c" title="Constant .">k</a>;
+<a name="l00668"></a><a class="code" href="classbdm_1_1migamma.html#c9847093da59a9ba0ebb68d2c592f5dc">00668</a>                         vec &amp;<a class="code" href="classbdm_1_1migamma.html#c9847093da59a9ba0ebb68d2c592f5dc" title="cache of epdf.alpha">_alpha</a>;
+<a name="l00670"></a><a class="code" href="classbdm_1_1migamma.html#0d854c047001b5465cf1ba21f52904b5">00670</a>                         vec &amp;<a class="code" href="classbdm_1_1migamma.html#0d854c047001b5465cf1ba21f52904b5" title="cache of epdf.beta">_beta</a>;
+<a name="l00671"></a>00671 
+<a name="l00672"></a>00672                 <span class="keyword">public</span>:
+<a name="l00675"></a>00675                         <a class="code" href="classbdm_1_1migamma.html" title="Inverse-Gamma random walk.">migamma</a> ( ) : <a class="code" href="classbdm_1_1mEF.html" title="Exponential family model.">mEF</a> (), <a class="code" href="classbdm_1_1epdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a> ( ), <a class="code" href="classbdm_1_1migamma.html#c9847093da59a9ba0ebb68d2c592f5dc" title="cache of epdf.alpha">_alpha</a> ( <a class="code" href="classbdm_1_1epdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a>.<a class="code" href="classbdm_1_1migamma.html#c9847093da59a9ba0ebb68d2c592f5dc" title="cache of epdf.alpha">_alpha</a>() ), <a class="code" href="classbdm_1_1migamma.html#0d854c047001b5465cf1ba21f52904b5" title="cache of epdf.beta">_beta</a> ( <a class="code" href="classbdm_1_1epdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a>.<a class="code" href="classbdm_1_1migamma.html#0d854c047001b5465cf1ba21f52904b5" title="cache of epdf.beta">_beta</a>() ) {<a class="code" href="classbdm_1_1mpdf.html#5eea43c56d38e4441bfb30270db949c0" title="pointer to internal epdf">ep</a>=&amp;<a class="code" href="classbdm_1_1epdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a>;};
+<a name="l00676"></a>00676                         <a class="code" href="classbdm_1_1migamma.html" title="Inverse-Gamma random walk.">migamma</a> ( <span class="keyword">const</span> <a class="code" href="classbdm_1_1migamma.html" title="Inverse-Gamma random walk.">migamma</a> &amp;m ) : <a class="code" href="classbdm_1_1mEF.html" title="Exponential family model.">mEF</a> (), <a class="code" href="classbdm_1_1epdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a> ( m.<a class="code" href="classbdm_1_1epdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a> ), <a class="code" href="classbdm_1_1migamma.html#c9847093da59a9ba0ebb68d2c592f5dc" title="cache of epdf.alpha">_alpha</a> ( <a class="code" href="classbdm_1_1epdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a>.<a class="code" href="classbdm_1_1migamma.html#c9847093da59a9ba0ebb68d2c592f5dc" title="cache of epdf.alpha">_alpha</a>() ), <a class="code" href="classbdm_1_1migamma.html#0d854c047001b5465cf1ba21f52904b5" title="cache of epdf.beta">_beta</a> ( <a class="code" href="classbdm_1_1epdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a>.<a class="code" href="classbdm_1_1migamma.html#0d854c047001b5465cf1ba21f52904b5" title="cache of epdf.beta">_beta</a>() ) {<a class="code" href="classbdm_1_1mpdf.html#5eea43c56d38e4441bfb30270db949c0" title="pointer to internal epdf">ep</a>=&amp;<a class="code" href="classbdm_1_1epdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a>;};
+<a name="l00678"></a>00678 
+<a name="l00680"></a><a class="code" href="classbdm_1_1migamma.html#8b10ab922e2a7bae2fb6bb3efc7b6151">00680</a>                         <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1migamma.html#8b10ab922e2a7bae2fb6bb3efc7b6151" title="Set value of k.">set_parameters</a> ( <span class="keywordtype">int</span> len, <span class="keywordtype">double</span> k0 )
+<a name="l00681"></a>00681                         {
+<a name="l00682"></a>00682                                 <a class="code" href="classbdm_1_1migamma.html#dc56bc9da542e0103ec16b9be8e5e38c" title="Constant .">k</a>=k0;
+<a name="l00683"></a>00683                                 <a class="code" href="classbdm_1_1epdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a>.set_parameters ( ( 1.0/ ( <a class="code" href="classbdm_1_1migamma.html#dc56bc9da542e0103ec16b9be8e5e38c" title="Constant .">k</a>*<a class="code" href="classbdm_1_1migamma.html#dc56bc9da542e0103ec16b9be8e5e38c" title="Constant .">k</a> ) +2.0 ) *ones ( len ) <span class="comment">/*alpha*/</span>, ones ( len ) <span class="comment">/*beta*/</span> );
+<a name="l00684"></a>00684                                 <a class="code" href="classbdm_1_1mpdf.html#7c1900976ff13dbc09c9729b3bbff9e6" title="dimension of the condition">dimc</a> = dimension();
+<a name="l00685"></a>00685                         };
+<a name="l00686"></a><a class="code" href="classbdm_1_1migamma.html#7a34b1e2e3aa2250d7c0ed7df1665b8c">00686</a>                         <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1migamma.html#7a34b1e2e3aa2250d7c0ed7df1665b8c" title="Update ep so that it represents this mpdf conditioned on rvc = cond.">condition</a> ( <span class="keyword">const</span> vec &amp;val )
+<a name="l00687"></a>00687                         {
+<a name="l00688"></a>00688                                 <a class="code" href="classbdm_1_1migamma.html#0d854c047001b5465cf1ba21f52904b5" title="cache of epdf.beta">_beta</a>=elem_mult ( val, ( <a class="code" href="classbdm_1_1migamma.html#c9847093da59a9ba0ebb68d2c592f5dc" title="cache of epdf.alpha">_alpha</a>-1.0 ) );
+<a name="l00689"></a>00689                         };
+<a name="l00690"></a>00690         };
 <a name="l00691"></a>00691 
-<a name="l00703"></a><a class="code" href="classbdm_1_1mgamma__fix.html">00703</a>         <span class="keyword">class </span><a class="code" href="classbdm_1_1mgamma__fix.html" title="Gamma random walk around a fixed point.">mgamma_fix</a> : <span class="keyword">public</span> <a class="code" href="classbdm_1_1mgamma.html" title="Gamma random walk.">mgamma</a>
-<a name="l00704"></a>00704         {
-<a name="l00705"></a>00705                 <span class="keyword">protected</span>:
-<a name="l00707"></a><a class="code" href="classbdm_1_1mgamma__fix.html#1eb701506aabb2e6af007e487212d6fa">00707</a>                         <span class="keywordtype">double</span> <a class="code" href="classbdm_1_1mgamma__fix.html#1eb701506aabb2e6af007e487212d6fa" title="parameter l">l</a>;
-<a name="l00709"></a><a class="code" href="classbdm_1_1mgamma__fix.html#018c6f901a04e419455308a07eb3b0b2">00709</a>                         vec <a class="code" href="classbdm_1_1mgamma__fix.html#018c6f901a04e419455308a07eb3b0b2" title="reference vector">refl</a>;
-<a name="l00710"></a>00710                 <span class="keyword">public</span>:
-<a name="l00712"></a><a class="code" href="classbdm_1_1mgamma__fix.html#9a31bc9b4b60188a18a2a6b588dc4b2d">00712</a>                         <a class="code" href="classbdm_1_1mgamma__fix.html#9a31bc9b4b60188a18a2a6b588dc4b2d" title="Constructor.">mgamma_fix</a> ( ) : <a class="code" href="classbdm_1_1mgamma.html" title="Gamma random walk.">mgamma</a> ( ),<a class="code" href="classbdm_1_1mgamma__fix.html#018c6f901a04e419455308a07eb3b0b2" title="reference vector">refl</a> () {};
-<a name="l00714"></a><a class="code" href="classbdm_1_1mgamma__fix.html#1bfd30e90db9dc1fbda4a9fbb0b716b2">00714</a>                         <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1mgamma__fix.html#1bfd30e90db9dc1fbda4a9fbb0b716b2" title="Set value of k.">set_parameters</a> ( <span class="keywordtype">double</span> k0 , vec ref0, <span class="keywordtype">double</span> l0 )
-<a name="l00715"></a>00715                         {
-<a name="l00716"></a>00716                                 <a class="code" href="classbdm_1_1mgamma__fix.html#1bfd30e90db9dc1fbda4a9fbb0b716b2" title="Set value of k.">mgamma::set_parameters</a> ( k0, ref0 );
-<a name="l00717"></a>00717                                 <a class="code" href="classbdm_1_1mgamma__fix.html#018c6f901a04e419455308a07eb3b0b2" title="reference vector">refl</a>=pow ( ref0,1.0-l0 );<a class="code" href="classbdm_1_1mgamma__fix.html#1eb701506aabb2e6af007e487212d6fa" title="parameter l">l</a>=l0;
-<a name="l00718"></a>00718                                 <a class="code" href="classbdm_1_1mpdf.html#7c1900976ff13dbc09c9729b3bbff9e6" title="dimension of the condition">dimc</a>=dimension();
-<a name="l00719"></a>00719                         };
-<a name="l00720"></a>00720 
-<a name="l00721"></a><a class="code" href="classbdm_1_1mgamma__fix.html#1d539591deb7a38bb3403c2b396c8ff7">00721</a>                         <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1mgamma__fix.html#1d539591deb7a38bb3403c2b396c8ff7" title="Update ep so that it represents this mpdf conditioned on rvc = cond.">condition</a> ( <span class="keyword">const</span> vec &amp;val ) {vec mean=elem_mult ( <a class="code" href="classbdm_1_1mgamma__fix.html#018c6f901a04e419455308a07eb3b0b2" title="reference vector">refl</a>,pow ( val,<a class="code" href="classbdm_1_1mgamma__fix.html#1eb701506aabb2e6af007e487212d6fa" title="parameter l">l</a> ) ); <a class="code" href="classbdm_1_1mgamma.html#3d95f4dde9214ff6dba265e18af60312" title="cache of epdf.beta">_beta</a>=<a class="code" href="classbdm_1_1mgamma.html#b20cf88cca1fe9b0b8f2a412608bfd09" title="Constant .">k</a>/mean;};
-<a name="l00722"></a>00722         };
-<a name="l00723"></a>00723 
+<a name="l00692"></a>00692 
+<a name="l00704"></a><a class="code" href="classbdm_1_1mgamma__fix.html">00704</a>         <span class="keyword">class </span><a class="code" href="classbdm_1_1mgamma__fix.html" title="Gamma random walk around a fixed point.">mgamma_fix</a> : <span class="keyword">public</span> <a class="code" href="classbdm_1_1mgamma.html" title="Gamma random walk.">mgamma</a>
+<a name="l00705"></a>00705         {
+<a name="l00706"></a>00706                 <span class="keyword">protected</span>:
+<a name="l00708"></a><a class="code" href="classbdm_1_1mgamma__fix.html#1eb701506aabb2e6af007e487212d6fa">00708</a>                         <span class="keywordtype">double</span> <a class="code" href="classbdm_1_1mgamma__fix.html#1eb701506aabb2e6af007e487212d6fa" title="parameter l">l</a>;
+<a name="l00710"></a><a class="code" href="classbdm_1_1mgamma__fix.html#018c6f901a04e419455308a07eb3b0b2">00710</a>                         vec <a class="code" href="classbdm_1_1mgamma__fix.html#018c6f901a04e419455308a07eb3b0b2" title="reference vector">refl</a>;
+<a name="l00711"></a>00711                 <span class="keyword">public</span>:
+<a name="l00713"></a><a class="code" href="classbdm_1_1mgamma__fix.html#9a31bc9b4b60188a18a2a6b588dc4b2d">00713</a>                         <a class="code" href="classbdm_1_1mgamma__fix.html#9a31bc9b4b60188a18a2a6b588dc4b2d" title="Constructor.">mgamma_fix</a> ( ) : <a class="code" href="classbdm_1_1mgamma.html" title="Gamma random walk.">mgamma</a> ( ),<a class="code" href="classbdm_1_1mgamma__fix.html#018c6f901a04e419455308a07eb3b0b2" title="reference vector">refl</a> () {};
+<a name="l00715"></a><a class="code" href="classbdm_1_1mgamma__fix.html#1bfd30e90db9dc1fbda4a9fbb0b716b2">00715</a>                         <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1mgamma__fix.html#1bfd30e90db9dc1fbda4a9fbb0b716b2" title="Set value of k.">set_parameters</a> ( <span class="keywordtype">double</span> k0 , vec ref0, <span class="keywordtype">double</span> l0 )
+<a name="l00716"></a>00716                         {
+<a name="l00717"></a>00717                                 <a class="code" href="classbdm_1_1mgamma__fix.html#1bfd30e90db9dc1fbda4a9fbb0b716b2" title="Set value of k.">mgamma::set_parameters</a> ( k0, ref0 );
+<a name="l00718"></a>00718                                 <a class="code" href="classbdm_1_1mgamma__fix.html#018c6f901a04e419455308a07eb3b0b2" title="reference vector">refl</a>=pow ( ref0,1.0-l0 );<a class="code" href="classbdm_1_1mgamma__fix.html#1eb701506aabb2e6af007e487212d6fa" title="parameter l">l</a>=l0;
+<a name="l00719"></a>00719                                 <a class="code" href="classbdm_1_1mpdf.html#7c1900976ff13dbc09c9729b3bbff9e6" title="dimension of the condition">dimc</a>=dimension();
+<a name="l00720"></a>00720                         };
+<a name="l00721"></a>00721 
+<a name="l00722"></a><a class="code" href="classbdm_1_1mgamma__fix.html#1d539591deb7a38bb3403c2b396c8ff7">00722</a>                         <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1mgamma__fix.html#1d539591deb7a38bb3403c2b396c8ff7" title="Update ep so that it represents this mpdf conditioned on rvc = cond.">condition</a> ( <span class="keyword">const</span> vec &amp;val ) {vec mean=elem_mult ( <a class="code" href="classbdm_1_1mgamma__fix.html#018c6f901a04e419455308a07eb3b0b2" title="reference vector">refl</a>,pow ( val,<a class="code" href="classbdm_1_1mgamma__fix.html#1eb701506aabb2e6af007e487212d6fa" title="parameter l">l</a> ) ); <a class="code" href="classbdm_1_1mgamma.html#3d95f4dde9214ff6dba265e18af60312" title="cache of epdf.beta">_beta</a>=<a class="code" href="classbdm_1_1mgamma.html#b20cf88cca1fe9b0b8f2a412608bfd09" title="Constant .">k</a>/mean;};
+<a name="l00723"></a>00723         };
 <a name="l00724"></a>00724 
-<a name="l00737"></a><a class="code" href="classbdm_1_1migamma__ref.html">00737</a>         <span class="keyword">class </span><a class="code" href="classbdm_1_1migamma__ref.html" title="Inverse-Gamma random walk around a fixed point.">migamma_ref</a> : <span class="keyword">public</span> <a class="code" href="classbdm_1_1migamma.html" title="Inverse-Gamma random walk.">migamma</a>
-<a name="l00738"></a>00738         {
-<a name="l00739"></a>00739                 <span class="keyword">protected</span>:
-<a name="l00741"></a><a class="code" href="classbdm_1_1migamma__ref.html#cdc1345ba8375fbdb18a69322d2f841d">00741</a>                         <span class="keywordtype">double</span> <a class="code" href="classbdm_1_1migamma__ref.html#cdc1345ba8375fbdb18a69322d2f841d" title="parameter l">l</a>;
-<a name="l00743"></a><a class="code" href="classbdm_1_1migamma__ref.html#3692dc67caf4367e15564d37f45476f6">00743</a>                         vec <a class="code" href="classbdm_1_1migamma__ref.html#3692dc67caf4367e15564d37f45476f6" title="reference vector">refl</a>;
-<a name="l00744"></a>00744                 <span class="keyword">public</span>:
-<a name="l00746"></a><a class="code" href="classbdm_1_1migamma__ref.html#f45b15a10f084991ba6b48295f10421f">00746</a>                         <a class="code" href="classbdm_1_1migamma__ref.html#f45b15a10f084991ba6b48295f10421f" title="Constructor.">migamma_ref</a> ( ) : <a class="code" href="classbdm_1_1migamma.html" title="Inverse-Gamma random walk.">migamma</a> (),<a class="code" href="classbdm_1_1migamma__ref.html#3692dc67caf4367e15564d37f45476f6" title="reference vector">refl</a> ( ) {};
-<a name="l00748"></a><a class="code" href="classbdm_1_1migamma__ref.html#b0b4eb278ef5d0831ec4954ba7bd2800">00748</a>                         <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1migamma__ref.html#b0b4eb278ef5d0831ec4954ba7bd2800" title="Set value of k.">set_parameters</a> ( <span class="keywordtype">double</span> k0 , vec ref0, <span class="keywordtype">double</span> l0 )
-<a name="l00749"></a>00749                         {
-<a name="l00750"></a>00750                                 <a class="code" href="classbdm_1_1migamma__ref.html#b0b4eb278ef5d0831ec4954ba7bd2800" title="Set value of k.">migamma::set_parameters</a> ( ref0.length(), k0 );
-<a name="l00751"></a>00751                                 <a class="code" href="classbdm_1_1migamma__ref.html#3692dc67caf4367e15564d37f45476f6" title="reference vector">refl</a>=pow ( ref0,1.0-l0 );
-<a name="l00752"></a>00752                                 <a class="code" href="classbdm_1_1migamma__ref.html#cdc1345ba8375fbdb18a69322d2f841d" title="parameter l">l</a>=l0;
-<a name="l00753"></a>00753                                 <a class="code" href="classbdm_1_1mpdf.html#7c1900976ff13dbc09c9729b3bbff9e6" title="dimension of the condition">dimc</a> = dimension();
-<a name="l00754"></a>00754                         };
-<a name="l00755"></a>00755 
-<a name="l00756"></a><a class="code" href="classbdm_1_1migamma__ref.html#ae86b2e4ff963d62e05d4e130514634a">00756</a>                         <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1migamma__ref.html#ae86b2e4ff963d62e05d4e130514634a" title="Update ep so that it represents this mpdf conditioned on rvc = cond.">condition</a> ( <span class="keyword">const</span> vec &amp;val )
-<a name="l00757"></a>00757                         {
-<a name="l00758"></a>00758                                 vec mean=elem_mult ( <a class="code" href="classbdm_1_1migamma__ref.html#3692dc67caf4367e15564d37f45476f6" title="reference vector">refl</a>,pow ( val,<a class="code" href="classbdm_1_1migamma__ref.html#cdc1345ba8375fbdb18a69322d2f841d" title="parameter l">l</a> ) );
-<a name="l00759"></a>00759                                 <a class="code" href="classbdm_1_1migamma__ref.html#ae86b2e4ff963d62e05d4e130514634a" title="Update ep so that it represents this mpdf conditioned on rvc = cond.">migamma::condition</a> ( mean );
-<a name="l00760"></a>00760                         };
-<a name="l00761"></a>00761 
-<a name="l00782"></a>00782                         <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1migamma__ref.html#6ab1cf56dd7b718c285ee65d8953cf3e">from_setting</a>( <span class="keyword">const</span> Setting &amp;root );
-<a name="l00783"></a>00783 
-<a name="l00784"></a>00784                         <span class="comment">// TODO dodelat void to_setting( Setting &amp;root ) const;</span>
-<a name="l00785"></a>00785         };
-<a name="l00786"></a>00786 
+<a name="l00725"></a>00725 
+<a name="l00738"></a><a class="code" href="classbdm_1_1migamma__ref.html">00738</a>         <span class="keyword">class </span><a class="code" href="classbdm_1_1migamma__ref.html" title="Inverse-Gamma random walk around a fixed point.">migamma_ref</a> : <span class="keyword">public</span> <a class="code" href="classbdm_1_1migamma.html" title="Inverse-Gamma random walk.">migamma</a>
+<a name="l00739"></a>00739         {
+<a name="l00740"></a>00740                 <span class="keyword">protected</span>:
+<a name="l00742"></a><a class="code" href="classbdm_1_1migamma__ref.html#cdc1345ba8375fbdb18a69322d2f841d">00742</a>                         <span class="keywordtype">double</span> <a class="code" href="classbdm_1_1migamma__ref.html#cdc1345ba8375fbdb18a69322d2f841d" title="parameter l">l</a>;
+<a name="l00744"></a><a class="code" href="classbdm_1_1migamma__ref.html#3692dc67caf4367e15564d37f45476f6">00744</a>                         vec <a class="code" href="classbdm_1_1migamma__ref.html#3692dc67caf4367e15564d37f45476f6" title="reference vector">refl</a>;
+<a name="l00745"></a>00745                 <span class="keyword">public</span>:
+<a name="l00747"></a><a class="code" href="classbdm_1_1migamma__ref.html#f45b15a10f084991ba6b48295f10421f">00747</a>                         <a class="code" href="classbdm_1_1migamma__ref.html#f45b15a10f084991ba6b48295f10421f" title="Constructor.">migamma_ref</a> ( ) : <a class="code" href="classbdm_1_1migamma.html" title="Inverse-Gamma random walk.">migamma</a> (),<a class="code" href="classbdm_1_1migamma__ref.html#3692dc67caf4367e15564d37f45476f6" title="reference vector">refl</a> ( ) {};
+<a name="l00749"></a><a class="code" href="classbdm_1_1migamma__ref.html#b0b4eb278ef5d0831ec4954ba7bd2800">00749</a>                         <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1migamma__ref.html#b0b4eb278ef5d0831ec4954ba7bd2800" title="Set value of k.">set_parameters</a> ( <span class="keywordtype">double</span> k0 , vec ref0, <span class="keywordtype">double</span> l0 )
+<a name="l00750"></a>00750                         {
+<a name="l00751"></a>00751                                 <a class="code" href="classbdm_1_1migamma__ref.html#b0b4eb278ef5d0831ec4954ba7bd2800" title="Set value of k.">migamma::set_parameters</a> ( ref0.length(), k0 );
+<a name="l00752"></a>00752                                 <a class="code" href="classbdm_1_1migamma__ref.html#3692dc67caf4367e15564d37f45476f6" title="reference vector">refl</a>=pow ( ref0,1.0-l0 );
+<a name="l00753"></a>00753                                 <a class="code" href="classbdm_1_1migamma__ref.html#cdc1345ba8375fbdb18a69322d2f841d" title="parameter l">l</a>=l0;
+<a name="l00754"></a>00754                                 <a class="code" href="classbdm_1_1mpdf.html#7c1900976ff13dbc09c9729b3bbff9e6" title="dimension of the condition">dimc</a> = dimension();
+<a name="l00755"></a>00755                         };
+<a name="l00756"></a>00756 
+<a name="l00757"></a><a class="code" href="classbdm_1_1migamma__ref.html#ae86b2e4ff963d62e05d4e130514634a">00757</a>                         <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1migamma__ref.html#ae86b2e4ff963d62e05d4e130514634a" title="Update ep so that it represents this mpdf conditioned on rvc = cond.">condition</a> ( <span class="keyword">const</span> vec &amp;val )
+<a name="l00758"></a>00758                         {
+<a name="l00759"></a>00759                                 vec mean=elem_mult ( <a class="code" href="classbdm_1_1migamma__ref.html#3692dc67caf4367e15564d37f45476f6" title="reference vector">refl</a>,pow ( val,<a class="code" href="classbdm_1_1migamma__ref.html#cdc1345ba8375fbdb18a69322d2f841d" title="parameter l">l</a> ) );
+<a name="l00760"></a>00760                                 <a class="code" href="classbdm_1_1migamma__ref.html#ae86b2e4ff963d62e05d4e130514634a" title="Update ep so that it represents this mpdf conditioned on rvc = cond.">migamma::condition</a> ( mean );
+<a name="l00761"></a>00761                         };
+<a name="l00762"></a>00762 
+<a name="l00783"></a>00783                         <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1migamma__ref.html#6ab1cf56dd7b718c285ee65d8953cf3e">from_setting</a>( <span class="keyword">const</span> Setting &amp;root );
+<a name="l00784"></a>00784 
+<a name="l00785"></a>00785                         <span class="comment">// TODO dodelat void to_setting( Setting &amp;root ) const;</span>
+<a name="l00786"></a>00786         };
 <a name="l00787"></a>00787 
-<a name="l00788"></a>00788         UIREGISTER(migamma_ref);
-<a name="l00789"></a>00789 
-<a name="l00799"></a><a class="code" href="classbdm_1_1elognorm.html">00799</a>         <span class="keyword">class </span><a class="code" href="classbdm_1_1elognorm.html">elognorm</a>: <span class="keyword">public</span> <a class="code" href="classbdm_1_1enorm.html" title="Gaussian density with positive definite (decomposed) covariance matrix.">enorm</a>&lt;ldmat&gt;
-<a name="l00800"></a>00800         {
-<a name="l00801"></a>00801                 <span class="keyword">public</span>:
-<a name="l00802"></a><a class="code" href="classbdm_1_1elognorm.html#8b948e2bce1253765a2542199913aaba">00802</a>                         vec <a class="code" href="classbdm_1_1elognorm.html#8b948e2bce1253765a2542199913aaba" title="Returns a sample,  from density .">sample</a>()<span class="keyword"> const </span>{<span class="keywordflow">return</span> exp ( <a class="code" href="classbdm_1_1enorm.html" title="Gaussian density with positive definite (decomposed) covariance matrix.">enorm&lt;ldmat&gt;::sample</a>() );};
-<a name="l00803"></a><a class="code" href="classbdm_1_1elognorm.html#adb41e4f4d6600dec6f8c1dbc5ed9eea">00803</a>                         vec <a class="code" href="classbdm_1_1elognorm.html#adb41e4f4d6600dec6f8c1dbc5ed9eea" title="return expected value">mean</a>()<span class="keyword"> const </span>{vec var=<a class="code" href="classbdm_1_1enorm.html#729c75ef0fa8abae03d58ad1f81e6773" title="return expected variance (not covariance!)">enorm&lt;ldmat&gt;::variance</a>();<span class="keywordflow">return</span> exp ( <a class="code" href="classbdm_1_1enorm.html#c702a194720853570d08b65482f842c7" title="mean value">mu</a> - 0.5*var );};
-<a name="l00804"></a>00804 
-<a name="l00805"></a>00805         };
-<a name="l00806"></a>00806 
-<a name="l00818"></a><a class="code" href="classbdm_1_1mlognorm.html">00818</a>         <span class="keyword">class </span><a class="code" href="classbdm_1_1mlognorm.html" title="Log-Normal random walk.">mlognorm</a> : <span class="keyword">public</span> <a class="code" href="classbdm_1_1mpdf.html" title="Conditional probability density, e.g. modeling some dependencies.">mpdf</a>
-<a name="l00819"></a>00819         {
-<a name="l00820"></a>00820                 <span class="keyword">protected</span>:
-<a name="l00821"></a>00821                         <a class="code" href="classbdm_1_1elognorm.html">elognorm</a> eno;
-<a name="l00823"></a><a class="code" href="classbdm_1_1mlognorm.html#a51128a2e503b8b2ce698244b9e0db1a">00823</a>                         <span class="keywordtype">double</span> <a class="code" href="classbdm_1_1mlognorm.html#a51128a2e503b8b2ce698244b9e0db1a" title="parameter 1/2*sigma^2">sig2</a>;
-<a name="l00825"></a><a class="code" href="classbdm_1_1mlognorm.html#7d0063f77d899ef22e8c5edd642176d2">00825</a>                         vec &amp;<a class="code" href="classbdm_1_1mlognorm.html#7d0063f77d899ef22e8c5edd642176d2" title="access">mu</a>;
-<a name="l00826"></a>00826                 <span class="keyword">public</span>:
-<a name="l00828"></a><a class="code" href="classbdm_1_1mlognorm.html#a5d6eb2688d02e0348b96c4fbd7bde41">00828</a>                         <a class="code" href="classbdm_1_1mlognorm.html#a5d6eb2688d02e0348b96c4fbd7bde41" title="Constructor.">mlognorm</a> ( ) : eno (), <a class="code" href="classbdm_1_1mlognorm.html#7d0063f77d899ef22e8c5edd642176d2" title="access">mu</a> ( eno._mu() ) {<a class="code" href="classbdm_1_1mpdf.html#5eea43c56d38e4441bfb30270db949c0" title="pointer to internal epdf">ep</a>=&amp;eno;};
-<a name="l00830"></a><a class="code" href="classbdm_1_1mlognorm.html#604cab0e8a76f9041dc3c606043bb39f">00830</a>                         <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1mlognorm.html#604cab0e8a76f9041dc3c606043bb39f" title="Set value of k.">set_parameters</a> ( <span class="keywordtype">int</span> size, <span class="keywordtype">double</span> k )
-<a name="l00831"></a>00831                         {
-<a name="l00832"></a>00832                                 <a class="code" href="classbdm_1_1mlognorm.html#a51128a2e503b8b2ce698244b9e0db1a" title="parameter 1/2*sigma^2">sig2</a> = 0.5*log ( k*k+1 );
-<a name="l00833"></a>00833                                 eno.<a class="code" href="classbdm_1_1enorm.html#b8322f2c11560871dd922c660f4771bb">set_parameters</a> ( zeros ( size ),2*<a class="code" href="classbdm_1_1mlognorm.html#a51128a2e503b8b2ce698244b9e0db1a" title="parameter 1/2*sigma^2">sig2</a>*eye ( size ) );
-<a name="l00834"></a>00834 
-<a name="l00835"></a>00835                                 <a class="code" href="classbdm_1_1mpdf.html#7c1900976ff13dbc09c9729b3bbff9e6" title="dimension of the condition">dimc</a> = size;
-<a name="l00836"></a>00836                         };
-<a name="l00837"></a>00837 
-<a name="l00838"></a><a class="code" href="classbdm_1_1mlognorm.html#9106d8fd8bdf2b6be675ffd8f3ca584e">00838</a>                         <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1mlognorm.html#9106d8fd8bdf2b6be675ffd8f3ca584e" title="Update ep so that it represents this mpdf conditioned on rvc = cond.">condition</a> ( <span class="keyword">const</span> vec &amp;val )
-<a name="l00839"></a>00839                         {
-<a name="l00840"></a>00840                                 <a class="code" href="classbdm_1_1mlognorm.html#7d0063f77d899ef22e8c5edd642176d2" title="access">mu</a>=log ( val )-<a class="code" href="classbdm_1_1mlognorm.html#a51128a2e503b8b2ce698244b9e0db1a" title="parameter 1/2*sigma^2">sig2</a>;<span class="comment">//elem_mult ( refl,pow ( val,l ) );</span>
-<a name="l00841"></a>00841                         };
-<a name="l00842"></a>00842 
-<a name="l00861"></a>00861                         <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1mlognorm.html#3a130457942be64ee9544e8dff00d09b">from_setting</a>( <span class="keyword">const</span> Setting &amp;root );
-<a name="l00862"></a>00862 
-<a name="l00863"></a>00863                         <span class="comment">// TODO dodelat void to_setting( Setting &amp;root ) const;</span>
-<a name="l00864"></a>00864 
-<a name="l00865"></a>00865         };
-<a name="l00866"></a>00866         
-<a name="l00867"></a>00867         UIREGISTER(mlognorm);
-<a name="l00868"></a>00868 
-<a name="l00872"></a><a class="code" href="classbdm_1_1eWishartCh.html">00872</a>         <span class="keyword">class </span><a class="code" href="classbdm_1_1eWishartCh.html">eWishartCh</a> : <span class="keyword">public</span> <a class="code" href="classbdm_1_1epdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a>
-<a name="l00873"></a>00873         {
-<a name="l00874"></a>00874                 <span class="keyword">protected</span>:
-<a name="l00876"></a><a class="code" href="classbdm_1_1eWishartCh.html#1b42f9284a32f23b0b253a628cda7490">00876</a>                         <a class="code" href="classchmat.html" title="Symmetric matrix stored in square root decomposition using upper cholesky.">chmat</a> <a class="code" href="classbdm_1_1eWishartCh.html#1b42f9284a32f23b0b253a628cda7490" title="Upper-Triagle of Choleski decomposition of .">Y</a>;
-<a name="l00878"></a><a class="code" href="classbdm_1_1eWishartCh.html#b745c73faef785009484180582050a1f">00878</a>                         <span class="keywordtype">int</span> <a class="code" href="classbdm_1_1eWishartCh.html#b745c73faef785009484180582050a1f" title="dimension of matrix ">p</a>;
-<a name="l00880"></a><a class="code" href="classbdm_1_1eWishartCh.html#1879a14d7d2bb05062523b189baa11c3">00880</a>                         <span class="keywordtype">double</span> <a class="code" href="classbdm_1_1eWishartCh.html#1879a14d7d2bb05062523b189baa11c3" title="degrees of freedom ">delta</a>;
-<a name="l00881"></a>00881                 <span class="keyword">public</span>:
-<a name="l00882"></a>00882                         <span class="keywordtype">void</span> set_parameters ( <span class="keyword">const</span> mat &amp;Y0, <span class="keyword">const</span> <span class="keywordtype">double</span> delta0 ) {<a class="code" href="classbdm_1_1eWishartCh.html#1b42f9284a32f23b0b253a628cda7490" title="Upper-Triagle of Choleski decomposition of .">Y</a>=<a class="code" href="classchmat.html" title="Symmetric matrix stored in square root decomposition using upper cholesky.">chmat</a> ( Y0 );<a class="code" href="classbdm_1_1eWishartCh.html#1879a14d7d2bb05062523b189baa11c3" title="degrees of freedom ">delta</a>=delta0; <a class="code" href="classbdm_1_1eWishartCh.html#b745c73faef785009484180582050a1f" title="dimension of matrix ">p</a>=<a class="code" href="classbdm_1_1eWishartCh.html#1b42f9284a32f23b0b253a628cda7490" title="Upper-Triagle of Choleski decomposition of .">Y</a>.<a class="code" href="classsqmat.html#071e80ced9cc3b8cbb360fa7462eb646" title="Reimplementing common functions of mat: cols().">rows</a>(); <a class="code" href="classbdm_1_1epdf.html#16adac20ec7fe07e1ea0b27d917788ce" title="dimension of the random variable">dim</a> = <a class="code" href="classbdm_1_1eWishartCh.html#b745c73faef785009484180582050a1f" title="dimension of matrix ">p</a>*<a class="code" href="classbdm_1_1eWishartCh.html#b745c73faef785009484180582050a1f" title="dimension of matrix ">p</a>; }
-<a name="l00883"></a>00883                         mat sample_mat()<span class="keyword"> const</span>
-<a name="l00884"></a>00884 <span class="keyword">                        </span>{
-<a name="l00885"></a>00885                                 mat X=zeros ( <a class="code" href="classbdm_1_1eWishartCh.html#b745c73faef785009484180582050a1f" title="dimension of matrix ">p</a>,<a class="code" href="classbdm_1_1eWishartCh.html#b745c73faef785009484180582050a1f" title="dimension of matrix ">p</a> );
-<a name="l00886"></a>00886 
-<a name="l00887"></a>00887                                 <span class="comment">//sample diagonal</span>
-<a name="l00888"></a>00888                                 <span class="keywordflow">for</span> ( <span class="keywordtype">int</span> i=0;i&lt;<a class="code" href="classbdm_1_1eWishartCh.html#b745c73faef785009484180582050a1f" title="dimension of matrix ">p</a>;i++ )
-<a name="l00889"></a>00889                                 {
-<a name="l00890"></a>00890                                         GamRNG.setup ( 0.5* ( <a class="code" href="classbdm_1_1eWishartCh.html#1879a14d7d2bb05062523b189baa11c3" title="degrees of freedom ">delta</a>-i ) , 0.5 ); <span class="comment">// no +1 !! index if from 0</span>
-<a name="l00891"></a>00891 <span class="preprocessor">#pragma omp critical</span>
-<a name="l00892"></a>00892 <span class="preprocessor"></span>                                        X ( i,i ) =sqrt ( GamRNG() );
-<a name="l00893"></a>00893                                 }
-<a name="l00894"></a>00894                                 <span class="comment">//do the rest</span>
-<a name="l00895"></a>00895                                 <span class="keywordflow">for</span> ( <span class="keywordtype">int</span> i=0;i&lt;p;i++ )
-<a name="l00896"></a>00896                                 {
-<a name="l00897"></a>00897                                         <span class="keywordflow">for</span> ( <span class="keywordtype">int</span> j=i+1;j&lt;p;j++ )
-<a name="l00898"></a>00898                                         {
-<a name="l00899"></a>00899 <span class="preprocessor">#pragma omp critical</span>
-<a name="l00900"></a>00900 <span class="preprocessor"></span>                                                X ( i,j ) =NorRNG.sample();
-<a name="l00901"></a>00901                                         }
-<a name="l00902"></a>00902                                 }
-<a name="l00903"></a>00903                                 <span class="keywordflow">return</span> X*<a class="code" href="classbdm_1_1eWishartCh.html#1b42f9284a32f23b0b253a628cda7490" title="Upper-Triagle of Choleski decomposition of .">Y</a>.<a class="code" href="classchmat.html#9c50d31c999d85d8e9d8cf2b69b6ac8c" title="Access function.">_Ch</a>();<span class="comment">// return upper triangular part of the decomposition</span>
-<a name="l00904"></a>00904                         }
-<a name="l00905"></a><a class="code" href="classbdm_1_1eWishartCh.html#8f2154b8b5be8f4c5788f261b6d57b9a">00905</a>                         vec <a class="code" href="classbdm_1_1eWishartCh.html#8f2154b8b5be8f4c5788f261b6d57b9a" title="Returns a sample,  from density .">sample</a> ()<span class="keyword"> const</span>
-<a name="l00906"></a>00906 <span class="keyword">                        </span>{
-<a name="l00907"></a>00907                                 <span class="keywordflow">return</span> vec ( sample_mat()._data(),p*p );
-<a name="l00908"></a>00908                         }
-<a name="l00910"></a><a class="code" href="classbdm_1_1eWishartCh.html#4eee757c0535c2a88bb20f0767c64981">00910</a>                         <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1eWishartCh.html#4eee757c0535c2a88bb20f0767c64981" title="fast access function y0 will be copied into Y.Ch.">setY</a> ( <span class="keyword">const</span> mat &amp;Ch0 ) {copy_vector ( <a class="code" href="classbdm_1_1epdf.html#16adac20ec7fe07e1ea0b27d917788ce" title="dimension of the random variable">dim</a>,Ch0._data(), <a class="code" href="classbdm_1_1eWishartCh.html#1b42f9284a32f23b0b253a628cda7490" title="Upper-Triagle of Choleski decomposition of .">Y</a>.<a class="code" href="classchmat.html#9c50d31c999d85d8e9d8cf2b69b6ac8c" title="Access function.">_Ch</a>()._data() );}
-<a name="l00912"></a><a class="code" href="classbdm_1_1eWishartCh.html#7eac414ec10b85aa5536b0092c57bc4a">00912</a>                         <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1eWishartCh.html#7eac414ec10b85aa5536b0092c57bc4a" title="fast access function y0 will be copied into Y.Ch.">_setY</a> ( <span class="keyword">const</span> vec &amp;ch0 ) {copy_vector ( <a class="code" href="classbdm_1_1epdf.html#16adac20ec7fe07e1ea0b27d917788ce" title="dimension of the random variable">dim</a>, ch0._data(), <a class="code" href="classbdm_1_1eWishartCh.html#1b42f9284a32f23b0b253a628cda7490" title="Upper-Triagle of Choleski decomposition of .">Y</a>.<a class="code" href="classchmat.html#9c50d31c999d85d8e9d8cf2b69b6ac8c" title="Access function.">_Ch</a>()._data() ); }
-<a name="l00914"></a><a class="code" href="classbdm_1_1eWishartCh.html#1708cacb5d8cb1b96395d35f5327cb7e">00914</a>                         <span class="keyword">const</span> <a class="code" href="classchmat.html" title="Symmetric matrix stored in square root decomposition using upper cholesky.">chmat</a>&amp; <a class="code" href="classbdm_1_1eWishartCh.html#1708cacb5d8cb1b96395d35f5327cb7e" title="access function">getY</a>()<span class="keyword">const </span>{<span class="keywordflow">return</span> <a class="code" href="classbdm_1_1eWishartCh.html#1b42f9284a32f23b0b253a628cda7490" title="Upper-Triagle of Choleski decomposition of .">Y</a>;}
-<a name="l00915"></a>00915         };
-<a name="l00916"></a>00916 
-<a name="l00917"></a>00917         <span class="keyword">class </span>eiWishartCh: <span class="keyword">public</span> epdf
-<a name="l00918"></a>00918         {
-<a name="l00919"></a>00919                 <span class="keyword">protected</span>:
-<a name="l00920"></a>00920                         eWishartCh W;
-<a name="l00921"></a>00921                         <span class="keywordtype">int</span> p;
-<a name="l00922"></a>00922                         <span class="keywordtype">double</span> delta;
-<a name="l00923"></a>00923                 <span class="keyword">public</span>:
-<a name="l00924"></a>00924                         <span class="keywordtype">void</span> set_parameters ( <span class="keyword">const</span> mat &amp;Y0, <span class="keyword">const</span> <span class="keywordtype">double</span> delta0) {
-<a name="l00925"></a>00925                                 delta = delta0;
-<a name="l00926"></a>00926                                 W.set_parameters ( inv ( Y0 ),delta0 ); 
-<a name="l00927"></a>00927                                 dim = W.dimension(); p=Y0.rows();
-<a name="l00928"></a>00928                         }
-<a name="l00929"></a>00929                         vec sample()<span class="keyword"> const </span>{mat iCh; iCh=inv ( W.sample_mat() ); <span class="keywordflow">return</span> vec ( iCh._data(),<a class="code" href="classbdm_1_1epdf.html#16adac20ec7fe07e1ea0b27d917788ce" title="dimension of the random variable">dim</a> );}
-<a name="l00930"></a>00930                         <span class="keywordtype">void</span> _setY ( <span class="keyword">const</span> vec &amp;y0 )
-<a name="l00931"></a>00931                         {
-<a name="l00932"></a>00932                                 mat Ch ( p,p );
-<a name="l00933"></a>00933                                 mat iCh ( p,p );
-<a name="l00934"></a>00934                                 copy_vector ( dim, y0._data(), Ch._data() );
-<a name="l00935"></a>00935                                 
-<a name="l00936"></a>00936                                 iCh=inv ( Ch );
-<a name="l00937"></a>00937                                 W.setY ( iCh );
-<a name="l00938"></a>00938                         }
-<a name="l00939"></a>00939                         <span class="keyword">virtual</span> <span class="keywordtype">double</span> evallog ( <span class="keyword">const</span> vec &amp;val )<span class="keyword"> const </span>{
-<a name="l00940"></a>00940                                 <a class="code" href="classchmat.html" title="Symmetric matrix stored in square root decomposition using upper cholesky.">chmat</a> X(p);
-<a name="l00941"></a>00941                                 <span class="keyword">const</span> <a class="code" href="classchmat.html" title="Symmetric matrix stored in square root decomposition using upper cholesky.">chmat</a>&amp; Y=W.getY();
-<a name="l00942"></a>00942                                  
-<a name="l00943"></a>00943                                 copy_vector(p*p,val._data(),X._Ch()._data());
-<a name="l00944"></a>00944                                 <a class="code" href="classchmat.html" title="Symmetric matrix stored in square root decomposition using upper cholesky.">chmat</a> iX(p);X.inv(iX);
-<a name="l00945"></a>00945                                 <span class="comment">// compute  </span>
-<a name="l00946"></a>00946 <span class="comment">//                              \frac{ |\Psi|^{m/2}|X|^{-(m+p+1)/2}e^{-tr(\Psi X^{-1})/2} }{ 2^{mp/2}\Gamma_p(m/2)},</span>
-<a name="l00947"></a>00947                                 mat M=Y.<a class="code" href="classchmat.html#045addd685f8d978efda232d7dcb070e" title="Conversion to full matrix.">to_mat</a>()*iX.to_mat();
-<a name="l00948"></a>00948                                 
-<a name="l00949"></a>00949                                 <span class="keywordtype">double</span> log1 = 0.5*p*(2*Y.<a class="code" href="classchmat.html#b504ca818203b13e667cb3c503980382" title="Logarithm of a determinant.">logdet</a>())-0.5*(delta+p+1)*(2*X.logdet())-0.5*trace(M); 
-<a name="l00950"></a>00950                                 <span class="comment">//Fixme! Multivariate gamma omitted!! it is ok for sampling, but not otherwise!!</span>
-<a name="l00951"></a>00951                                 
-<a name="l00952"></a>00952 <span class="comment">/*                              if (0) {</span>
-<a name="l00953"></a>00953 <span class="comment">                                        mat XX=X.to_mat();</span>
-<a name="l00954"></a>00954 <span class="comment">                                        mat YY=Y.to_mat();</span>
-<a name="l00955"></a>00955 <span class="comment">                                        </span>
-<a name="l00956"></a>00956 <span class="comment">                                        double log2 = 0.5*p*log(det(YY))-0.5*(delta+p+1)*log(det(XX))-0.5*trace(YY*inv(XX)); </span>
-<a name="l00957"></a>00957 <span class="comment">                                        cout &lt;&lt; log1 &lt;&lt; "," &lt;&lt; log2 &lt;&lt; endl;</span>
-<a name="l00958"></a>00958 <span class="comment">                                }*/</span>
-<a name="l00959"></a>00959                                 <span class="keywordflow">return</span> log1;                            
-<a name="l00960"></a>00960                         };
-<a name="l00961"></a>00961                         
-<a name="l00962"></a>00962         };
-<a name="l00963"></a>00963 
-<a name="l00964"></a>00964         <span class="keyword">class </span>rwiWishartCh : <span class="keyword">public</span> mpdf
-<a name="l00965"></a>00965         {
-<a name="l00966"></a>00966                 <span class="keyword">protected</span>:
-<a name="l00967"></a>00967                         eiWishartCh eiW;
-<a name="l00969"></a>00969                         <span class="keywordtype">double</span> sqd;
-<a name="l00970"></a>00970                         <span class="comment">//reference point for diagonal</span>
-<a name="l00971"></a>00971                         vec refl;
-<a name="l00972"></a>00972                         <span class="keywordtype">double</span> l;
-<a name="l00973"></a>00973                         <span class="keywordtype">int</span> p;
-<a name="l00974"></a>00974                 <span class="keyword">public</span>:
-<a name="l00975"></a>00975                         <span class="keywordtype">void</span> set_parameters ( <span class="keywordtype">int</span> p0, <span class="keywordtype">double</span> k, vec ref0, <span class="keywordtype">double</span> l0  )
-<a name="l00976"></a>00976                         {
-<a name="l00977"></a>00977                                 p=p0;
-<a name="l00978"></a>00978                                 <span class="keywordtype">double</span> delta = 2/(k*k)+p+3;
-<a name="l00979"></a>00979                                 sqd=sqrt ( delta-p-1 );
-<a name="l00980"></a>00980                                 l=l0;
-<a name="l00981"></a>00981                                 refl=pow(ref0,1-l);
-<a name="l00982"></a>00982                                 
-<a name="l00983"></a>00983                                 eiW.set_parameters ( eye ( p ),delta );
-<a name="l00984"></a>00984                                 <a class="code" href="classbdm_1_1mpdf.html#5eea43c56d38e4441bfb30270db949c0" title="pointer to internal epdf">ep</a>=&amp;eiW;
-<a name="l00985"></a>00985                                 <a class="code" href="classbdm_1_1mpdf.html#7c1900976ff13dbc09c9729b3bbff9e6" title="dimension of the condition">dimc</a>=eiW.dimension();
-<a name="l00986"></a>00986                         }
-<a name="l00987"></a>00987                         <span class="keywordtype">void</span> condition ( <span class="keyword">const</span> vec &amp;c ) {
-<a name="l00988"></a>00988                                 vec z=c;
-<a name="l00989"></a>00989                                 <span class="keywordtype">int</span> ri=0;
-<a name="l00990"></a>00990                                 <span class="keywordflow">for</span>(<span class="keywordtype">int</span> i=0;i&lt;p*p;i+=(p+1)){<span class="comment">//trace diagonal element</span>
-<a name="l00991"></a>00991                                         z(i) = pow(z(i),l)*refl(ri);
-<a name="l00992"></a>00992                                         ri++;
-<a name="l00993"></a>00993                                 }
-<a name="l00994"></a>00994 
-<a name="l00995"></a>00995                                 eiW._setY ( sqd*z );
-<a name="l00996"></a>00996                         }
-<a name="l00997"></a>00997         };
-<a name="l00998"></a>00998 
-<a name="l01000"></a>01000         <span class="keyword">enum</span> RESAMPLING_METHOD { MULTINOMIAL = 0, STRATIFIED = 1, SYSTEMATIC = 3 };
-<a name="l01006"></a><a class="code" href="classbdm_1_1eEmp.html">01006</a>         <span class="keyword">class </span><a class="code" href="classbdm_1_1eEmp.html" title="Weighted empirical density.">eEmp</a>: <span class="keyword">public</span> <a class="code" href="classbdm_1_1epdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a>
-<a name="l01007"></a>01007         {
-<a name="l01008"></a>01008                 <span class="keyword">protected</span> :
-<a name="l01010"></a><a class="code" href="classbdm_1_1eEmp.html#9798006271ca77629855113f1283a031">01010</a>                         <span class="keywordtype">int</span> <a class="code" href="classbdm_1_1eEmp.html#9798006271ca77629855113f1283a031" title="Number of particles.">n</a>;
-<a name="l01012"></a><a class="code" href="classbdm_1_1eEmp.html#9d39241aab7c4bbeb07c6d516421c67d">01012</a>                         vec <a class="code" href="classbdm_1_1eEmp.html#9d39241aab7c4bbeb07c6d516421c67d" title="Sample weights .">w</a>;
-<a name="l01014"></a><a class="code" href="classbdm_1_1eEmp.html#73d819553a0f268b055a087d2d4486f3">01014</a>                         Array&lt;vec&gt; <a class="code" href="classbdm_1_1eEmp.html#73d819553a0f268b055a087d2d4486f3" title="Samples .">samples</a>;
-<a name="l01015"></a>01015                 <span class="keyword">public</span>:
-<a name="l01018"></a>01018                         <a class="code" href="classbdm_1_1eEmp.html" title="Weighted empirical density.">eEmp</a> ( ) :<a class="code" href="classbdm_1_1epdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a> ( ),<a class="code" href="classbdm_1_1eEmp.html#9d39241aab7c4bbeb07c6d516421c67d" title="Sample weights .">w</a> ( ),<a class="code" href="classbdm_1_1eEmp.html#73d819553a0f268b055a087d2d4486f3" title="Samples .">samples</a> ( ) {};
-<a name="l01019"></a>01019                         <a class="code" href="classbdm_1_1eEmp.html" title="Weighted empirical density.">eEmp</a> ( <span class="keyword">const</span> <a class="code" href="classbdm_1_1eEmp.html" title="Weighted empirical density.">eEmp</a> &amp;e ) : <a class="code" href="classbdm_1_1epdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a> ( e ), <a class="code" href="classbdm_1_1eEmp.html#9d39241aab7c4bbeb07c6d516421c67d" title="Sample weights .">w</a> ( e.<a class="code" href="classbdm_1_1eEmp.html#9d39241aab7c4bbeb07c6d516421c67d" title="Sample weights .">w</a> ), <a class="code" href="classbdm_1_1eEmp.html#73d819553a0f268b055a087d2d4486f3" title="Samples .">samples</a> ( e.<a class="code" href="classbdm_1_1eEmp.html#73d819553a0f268b055a087d2d4486f3" title="Samples .">samples</a> ) {};
-<a name="l01021"></a>01021 
-<a name="l01023"></a>01023                         <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1eEmp.html#7cfd383180b486fe4526bdf0179350c0" title="Set samples and weights.">set_statistics</a> ( <span class="keyword">const</span> vec &amp;w0, <span class="keyword">const</span> epdf* pdf0 );
-<a name="l01025"></a><a class="code" href="classbdm_1_1eEmp.html#cef74aa5f87d10d440b9b1e8bc78c1e7">01025</a>                         <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1eEmp.html#cef74aa5f87d10d440b9b1e8bc78c1e7" title="Set samples and weights.">set_statistics</a> ( <span class="keyword">const</span> <a class="code" href="classbdm_1_1epdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a>* pdf0 , <span class="keywordtype">int</span> <a class="code" href="classbdm_1_1eEmp.html#9798006271ca77629855113f1283a031" title="Number of particles.">n</a> ) {<a class="code" href="classbdm_1_1eEmp.html#cef74aa5f87d10d440b9b1e8bc78c1e7" title="Set samples and weights.">set_statistics</a> ( ones ( n ) /n,pdf0 );};
-<a name="l01027"></a>01027                         <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1eEmp.html#b62d802b8ef39f7c4dcbeb366c90951a" title="Set sample.">set_samples</a> ( <span class="keyword">const</span> <a class="code" href="classbdm_1_1epdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a>* pdf0 );
-<a name="l01029"></a><a class="code" href="classbdm_1_1eEmp.html#c74c281d652356c19b6b079e42ca7ef1">01029</a>                         <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1eEmp.html#c74c281d652356c19b6b079e42ca7ef1" title="Set sample.">set_parameters</a> ( <span class="keywordtype">int</span> n0, <span class="keywordtype">bool</span> copy=<span class="keyword">true</span> ) {<a class="code" href="classbdm_1_1eEmp.html#9798006271ca77629855113f1283a031" title="Number of particles.">n</a>=n0; <a class="code" href="classbdm_1_1eEmp.html#9d39241aab7c4bbeb07c6d516421c67d" title="Sample weights .">w</a>.set_size ( n0,copy );<a class="code" href="classbdm_1_1eEmp.html#73d819553a0f268b055a087d2d4486f3" title="Samples .">samples</a>.set_size ( n0,copy );};
-<a name="l01031"></a><a class="code" href="classbdm_1_1eEmp.html#d7f83cc0415cd44ae7cc8b4bdad93aef">01031</a>                         vec&amp; <a class="code" href="classbdm_1_1eEmp.html#d7f83cc0415cd44ae7cc8b4bdad93aef" title="Potentially dangerous, use with care.">_w</a>()  {<span class="keywordflow">return</span> <a class="code" href="classbdm_1_1eEmp.html#9d39241aab7c4bbeb07c6d516421c67d" title="Sample weights .">w</a>;};
-<a name="l01033"></a><a class="code" href="classbdm_1_1eEmp.html#b7d7106f486e3fad38590914a693d714">01033</a>                         <span class="keyword">const</span> vec&amp; <a class="code" href="classbdm_1_1eEmp.html#b7d7106f486e3fad38590914a693d714" title="Potentially dangerous, use with care.">_w</a>()<span class="keyword"> const </span>{<span class="keywordflow">return</span> <a class="code" href="classbdm_1_1eEmp.html#9d39241aab7c4bbeb07c6d516421c67d" title="Sample weights .">w</a>;};
-<a name="l01035"></a><a class="code" href="classbdm_1_1eEmp.html#c24966b0aaeb767bc8a6b4fd60931be2">01035</a>                         Array&lt;vec&gt;&amp; <a class="code" href="classbdm_1_1eEmp.html#c24966b0aaeb767bc8a6b4fd60931be2" title="access function">_samples</a>() {<span class="keywordflow">return</span> <a class="code" href="classbdm_1_1eEmp.html#73d819553a0f268b055a087d2d4486f3" title="Samples .">samples</a>;};
-<a name="l01037"></a><a class="code" href="classbdm_1_1eEmp.html#b59af0efdb009d98ea8ebfa965e74ae2">01037</a>                         <span class="keyword">const</span> Array&lt;vec&gt;&amp; <a class="code" href="classbdm_1_1eEmp.html#b59af0efdb009d98ea8ebfa965e74ae2" title="access function">_samples</a>()<span class="keyword"> const </span>{<span class="keywordflow">return</span> <a class="code" href="classbdm_1_1eEmp.html#73d819553a0f268b055a087d2d4486f3" title="Samples .">samples</a>;};
-<a name="l01039"></a>01039                         ivec <a class="code" href="classbdm_1_1eEmp.html#f06ce255de5dbb2313f52ee51f82ba3d" title="Function performs resampling, i.e. removal of low-weight samples and duplication...">resample</a> ( RESAMPLING_METHOD method=SYSTEMATIC );
-<a name="l01041"></a><a class="code" href="classbdm_1_1eEmp.html#97f1e07b5ae6eebc91c7365f0f88d270">01041</a>                         vec <a class="code" href="classbdm_1_1eEmp.html#97f1e07b5ae6eebc91c7365f0f88d270" title="inherited operation : NOT implemneted">sample</a>()<span class="keyword"> const </span>{it_error ( <span class="stringliteral">"Not implemented"</span> );<span class="keywordflow">return</span> 0;}
-<a name="l01043"></a><a class="code" href="classbdm_1_1eEmp.html#01654c014d3aa068f8d4ecba4be86d09">01043</a>                         <span class="keywordtype">double</span> <a class="code" href="classbdm_1_1eEmp.html#01654c014d3aa068f8d4ecba4be86d09" title="inherited operation : NOT implemneted">evallog</a> ( <span class="keyword">const</span> vec &amp;val )<span class="keyword"> const </span>{it_error ( <span class="stringliteral">"Not implemented"</span> );<span class="keywordflow">return</span> 0.0;}
-<a name="l01044"></a><a class="code" href="classbdm_1_1eEmp.html#bbfcb4f868c7381298c281a256d8c4b9">01044</a>                         vec <a class="code" href="classbdm_1_1eEmp.html#bbfcb4f868c7381298c281a256d8c4b9" title="return expected value">mean</a>()<span class="keyword"> const</span>
-<a name="l01045"></a>01045 <span class="keyword">                        </span>{
-<a name="l01046"></a>01046                                 vec pom=zeros ( <a class="code" href="classbdm_1_1epdf.html#16adac20ec7fe07e1ea0b27d917788ce" title="dimension of the random variable">dim</a> );
-<a name="l01047"></a>01047                                 <span class="keywordflow">for</span> ( <span class="keywordtype">int</span> i=0;i&lt;<a class="code" href="classbdm_1_1eEmp.html#9798006271ca77629855113f1283a031" title="Number of particles.">n</a>;i++ ) {pom+=<a class="code" href="classbdm_1_1eEmp.html#73d819553a0f268b055a087d2d4486f3" title="Samples .">samples</a> ( i ) *<a class="code" href="classbdm_1_1eEmp.html#9d39241aab7c4bbeb07c6d516421c67d" title="Sample weights .">w</a> ( i );}
-<a name="l01048"></a>01048                                 <span class="keywordflow">return</span> pom;
-<a name="l01049"></a>01049                         }
-<a name="l01050"></a><a class="code" href="classbdm_1_1eEmp.html#05e9ebf467ede737cb6a3621d7fd3c87">01050</a>                         vec <a class="code" href="classbdm_1_1eEmp.html#05e9ebf467ede737cb6a3621d7fd3c87" title="return expected variance (not covariance!)">variance</a>()<span class="keyword"> const</span>
-<a name="l01051"></a>01051 <span class="keyword">                        </span>{
-<a name="l01052"></a>01052                                 vec pom=zeros ( <a class="code" href="classbdm_1_1epdf.html#16adac20ec7fe07e1ea0b27d917788ce" title="dimension of the random variable">dim</a> );
-<a name="l01053"></a>01053                                 <span class="keywordflow">for</span> ( <span class="keywordtype">int</span> i=0;i&lt;<a class="code" href="classbdm_1_1eEmp.html#9798006271ca77629855113f1283a031" title="Number of particles.">n</a>;i++ ) {pom+=pow ( <a class="code" href="classbdm_1_1eEmp.html#73d819553a0f268b055a087d2d4486f3" title="Samples .">samples</a> ( i ),2 ) *<a class="code" href="classbdm_1_1eEmp.html#9d39241aab7c4bbeb07c6d516421c67d" title="Sample weights .">w</a> ( i );}
-<a name="l01054"></a>01054                                 <span class="keywordflow">return</span> pom-pow ( <a class="code" href="classbdm_1_1eEmp.html#bbfcb4f868c7381298c281a256d8c4b9" title="return expected value">mean</a>(),2 );
-<a name="l01055"></a>01055                         }
-<a name="l01057"></a><a class="code" href="classbdm_1_1eEmp.html#b1c9df656144edf79ba2d885613f661f">01057</a>                         <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1eEmp.html#b1c9df656144edf79ba2d885613f661f" title="For this class, qbounds are minimum and maximum value of the population!">qbounds</a> ( vec &amp;lb, vec &amp;ub, <span class="keywordtype">double</span> perc=0.95 )<span class="keyword"> const</span>
-<a name="l01058"></a>01058 <span class="keyword">                        </span>{
-<a name="l01059"></a>01059                                 <span class="comment">// lb in inf so than it will be pushed below;</span>
-<a name="l01060"></a>01060                                 lb.set_size ( <a class="code" href="classbdm_1_1epdf.html#16adac20ec7fe07e1ea0b27d917788ce" title="dimension of the random variable">dim</a> );
-<a name="l01061"></a>01061                                 ub.set_size ( <a class="code" href="classbdm_1_1epdf.html#16adac20ec7fe07e1ea0b27d917788ce" title="dimension of the random variable">dim</a> );
-<a name="l01062"></a>01062                                 lb = std::numeric_limits&lt;double&gt;::infinity();
-<a name="l01063"></a>01063                                 ub = -std::numeric_limits&lt;double&gt;::infinity();
-<a name="l01064"></a>01064                                 <span class="keywordtype">int</span> j;
-<a name="l01065"></a>01065                                 <span class="keywordflow">for</span> ( <span class="keywordtype">int</span> i=0;i&lt;<a class="code" href="classbdm_1_1eEmp.html#9798006271ca77629855113f1283a031" title="Number of particles.">n</a>;i++ )
-<a name="l01066"></a>01066                                 {
-<a name="l01067"></a>01067                                         <span class="keywordflow">for</span> ( j=0;j&lt;<a class="code" href="classbdm_1_1epdf.html#16adac20ec7fe07e1ea0b27d917788ce" title="dimension of the random variable">dim</a>; j++ )
-<a name="l01068"></a>01068                                         {
-<a name="l01069"></a>01069                                                 <span class="keywordflow">if</span> ( <a class="code" href="classbdm_1_1eEmp.html#73d819553a0f268b055a087d2d4486f3" title="Samples .">samples</a> ( i ) ( j ) &lt;lb ( j ) ) {lb ( j ) =<a class="code" href="classbdm_1_1eEmp.html#73d819553a0f268b055a087d2d4486f3" title="Samples .">samples</a> ( i ) ( j );}
-<a name="l01070"></a>01070                                                 <span class="keywordflow">if</span> ( <a class="code" href="classbdm_1_1eEmp.html#73d819553a0f268b055a087d2d4486f3" title="Samples .">samples</a> ( i ) ( j ) &gt;ub ( j ) ) {ub ( j ) =<a class="code" href="classbdm_1_1eEmp.html#73d819553a0f268b055a087d2d4486f3" title="Samples .">samples</a> ( i ) ( j );}
-<a name="l01071"></a>01071                                         }
-<a name="l01072"></a>01072                                 }
-<a name="l01073"></a>01073                         }
-<a name="l01074"></a>01074         };
-<a name="l01075"></a>01075 
-<a name="l01076"></a>01076 
+<a name="l00788"></a>00788 
+<a name="l00789"></a>00789         UIREGISTER(migamma_ref);
+<a name="l00790"></a>00790 
+<a name="l00800"></a><a class="code" href="classbdm_1_1elognorm.html">00800</a>         <span class="keyword">class </span><a class="code" href="classbdm_1_1elognorm.html">elognorm</a>: <span class="keyword">public</span> <a class="code" href="classbdm_1_1enorm.html" title="Gaussian density with positive definite (decomposed) covariance matrix.">enorm</a>&lt;ldmat&gt;
+<a name="l00801"></a>00801         {
+<a name="l00802"></a>00802                 <span class="keyword">public</span>:
+<a name="l00803"></a><a class="code" href="classbdm_1_1elognorm.html#8b948e2bce1253765a2542199913aaba">00803</a>                         vec <a class="code" href="classbdm_1_1elognorm.html#8b948e2bce1253765a2542199913aaba" title="Returns a sample,  from density .">sample</a>()<span class="keyword"> const </span>{<span class="keywordflow">return</span> exp ( <a class="code" href="classbdm_1_1enorm.html" title="Gaussian density with positive definite (decomposed) covariance matrix.">enorm&lt;ldmat&gt;::sample</a>() );};
+<a name="l00804"></a><a class="code" href="classbdm_1_1elognorm.html#adb41e4f4d6600dec6f8c1dbc5ed9eea">00804</a>                         vec <a class="code" href="classbdm_1_1elognorm.html#adb41e4f4d6600dec6f8c1dbc5ed9eea" title="return expected value">mean</a>()<span class="keyword"> const </span>{vec var=<a class="code" href="classbdm_1_1enorm.html#729c75ef0fa8abae03d58ad1f81e6773" title="return expected variance (not covariance!)">enorm&lt;ldmat&gt;::variance</a>();<span class="keywordflow">return</span> exp ( <a class="code" href="classbdm_1_1enorm.html#c702a194720853570d08b65482f842c7" title="mean value">mu</a> - 0.5*var );};
+<a name="l00805"></a>00805 
+<a name="l00806"></a>00806         };
+<a name="l00807"></a>00807 
+<a name="l00819"></a><a class="code" href="classbdm_1_1mlognorm.html">00819</a>         <span class="keyword">class </span><a class="code" href="classbdm_1_1mlognorm.html" title="Log-Normal random walk.">mlognorm</a> : <span class="keyword">public</span> <a class="code" href="classbdm_1_1mpdf.html" title="Conditional probability density, e.g. modeling some dependencies.">mpdf</a>
+<a name="l00820"></a>00820         {
+<a name="l00821"></a>00821                 <span class="keyword">protected</span>:
+<a name="l00822"></a>00822                         <a class="code" href="classbdm_1_1elognorm.html">elognorm</a> eno;
+<a name="l00824"></a><a class="code" href="classbdm_1_1mlognorm.html#a51128a2e503b8b2ce698244b9e0db1a">00824</a>                         <span class="keywordtype">double</span> <a class="code" href="classbdm_1_1mlognorm.html#a51128a2e503b8b2ce698244b9e0db1a" title="parameter 1/2*sigma^2">sig2</a>;
+<a name="l00826"></a><a class="code" href="classbdm_1_1mlognorm.html#7d0063f77d899ef22e8c5edd642176d2">00826</a>                         vec &amp;<a class="code" href="classbdm_1_1mlognorm.html#7d0063f77d899ef22e8c5edd642176d2" title="access">mu</a>;
+<a name="l00827"></a>00827                 <span class="keyword">public</span>:
+<a name="l00829"></a><a class="code" href="classbdm_1_1mlognorm.html#a5d6eb2688d02e0348b96c4fbd7bde41">00829</a>                         <a class="code" href="classbdm_1_1mlognorm.html#a5d6eb2688d02e0348b96c4fbd7bde41" title="Constructor.">mlognorm</a> ( ) : eno (), <a class="code" href="classbdm_1_1mlognorm.html#7d0063f77d899ef22e8c5edd642176d2" title="access">mu</a> ( eno._mu() ) {<a class="code" href="classbdm_1_1mpdf.html#5eea43c56d38e4441bfb30270db949c0" title="pointer to internal epdf">ep</a>=&amp;eno;};
+<a name="l00831"></a><a class="code" href="classbdm_1_1mlognorm.html#604cab0e8a76f9041dc3c606043bb39f">00831</a>                         <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1mlognorm.html#604cab0e8a76f9041dc3c606043bb39f" title="Set value of k.">set_parameters</a> ( <span class="keywordtype">int</span> size, <span class="keywordtype">double</span> k )
+<a name="l00832"></a>00832                         {
+<a name="l00833"></a>00833                                 <a class="code" href="classbdm_1_1mlognorm.html#a51128a2e503b8b2ce698244b9e0db1a" title="parameter 1/2*sigma^2">sig2</a> = 0.5*log ( k*k+1 );
+<a name="l00834"></a>00834                                 eno.<a class="code" href="classbdm_1_1enorm.html#b8322f2c11560871dd922c660f4771bb">set_parameters</a> ( zeros ( size ),2*<a class="code" href="classbdm_1_1mlognorm.html#a51128a2e503b8b2ce698244b9e0db1a" title="parameter 1/2*sigma^2">sig2</a>*eye ( size ) );
+<a name="l00835"></a>00835 
+<a name="l00836"></a>00836                                 <a class="code" href="classbdm_1_1mpdf.html#7c1900976ff13dbc09c9729b3bbff9e6" title="dimension of the condition">dimc</a> = size;
+<a name="l00837"></a>00837                         };
+<a name="l00838"></a>00838 
+<a name="l00839"></a><a class="code" href="classbdm_1_1mlognorm.html#9106d8fd8bdf2b6be675ffd8f3ca584e">00839</a>                         <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1mlognorm.html#9106d8fd8bdf2b6be675ffd8f3ca584e" title="Update ep so that it represents this mpdf conditioned on rvc = cond.">condition</a> ( <span class="keyword">const</span> vec &amp;val )
+<a name="l00840"></a>00840                         {
+<a name="l00841"></a>00841                                 <a class="code" href="classbdm_1_1mlognorm.html#7d0063f77d899ef22e8c5edd642176d2" title="access">mu</a>=log ( val )-<a class="code" href="classbdm_1_1mlognorm.html#a51128a2e503b8b2ce698244b9e0db1a" title="parameter 1/2*sigma^2">sig2</a>;<span class="comment">//elem_mult ( refl,pow ( val,l ) );</span>
+<a name="l00842"></a>00842                         };
+<a name="l00843"></a>00843 
+<a name="l00862"></a>00862                         <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1mlognorm.html#3a130457942be64ee9544e8dff00d09b">from_setting</a>( <span class="keyword">const</span> Setting &amp;root );
+<a name="l00863"></a>00863 
+<a name="l00864"></a>00864                         <span class="comment">// TODO dodelat void to_setting( Setting &amp;root ) const;</span>
+<a name="l00865"></a>00865 
+<a name="l00866"></a>00866         };
+<a name="l00867"></a>00867         
+<a name="l00868"></a>00868         UIREGISTER(mlognorm);
+<a name="l00869"></a>00869 
+<a name="l00873"></a><a class="code" href="classbdm_1_1eWishartCh.html">00873</a>         <span class="keyword">class </span><a class="code" href="classbdm_1_1eWishartCh.html">eWishartCh</a> : <span class="keyword">public</span> <a class="code" href="classbdm_1_1epdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a>
+<a name="l00874"></a>00874         {
+<a name="l00875"></a>00875                 <span class="keyword">protected</span>:
+<a name="l00877"></a><a class="code" href="classbdm_1_1eWishartCh.html#1b42f9284a32f23b0b253a628cda7490">00877</a>                         <a class="code" href="classchmat.html" title="Symmetric matrix stored in square root decomposition using upper cholesky.">chmat</a> <a class="code" href="classbdm_1_1eWishartCh.html#1b42f9284a32f23b0b253a628cda7490" title="Upper-Triagle of Choleski decomposition of .">Y</a>;
+<a name="l00879"></a><a class="code" href="classbdm_1_1eWishartCh.html#b745c73faef785009484180582050a1f">00879</a>                         <span class="keywordtype">int</span> <a class="code" href="classbdm_1_1eWishartCh.html#b745c73faef785009484180582050a1f" title="dimension of matrix ">p</a>;
+<a name="l00881"></a><a class="code" href="classbdm_1_1eWishartCh.html#1879a14d7d2bb05062523b189baa11c3">00881</a>                         <span class="keywordtype">double</span> <a class="code" href="classbdm_1_1eWishartCh.html#1879a14d7d2bb05062523b189baa11c3" title="degrees of freedom ">delta</a>;
+<a name="l00882"></a>00882                 <span class="keyword">public</span>:
+<a name="l00883"></a>00883                         <span class="keywordtype">void</span> set_parameters ( <span class="keyword">const</span> mat &amp;Y0, <span class="keyword">const</span> <span class="keywordtype">double</span> delta0 ) {<a class="code" href="classbdm_1_1eWishartCh.html#1b42f9284a32f23b0b253a628cda7490" title="Upper-Triagle of Choleski decomposition of .">Y</a>=<a class="code" href="classchmat.html" title="Symmetric matrix stored in square root decomposition using upper cholesky.">chmat</a> ( Y0 );<a class="code" href="classbdm_1_1eWishartCh.html#1879a14d7d2bb05062523b189baa11c3" title="degrees of freedom ">delta</a>=delta0; <a class="code" href="classbdm_1_1eWishartCh.html#b745c73faef785009484180582050a1f" title="dimension of matrix ">p</a>=<a class="code" href="classbdm_1_1eWishartCh.html#1b42f9284a32f23b0b253a628cda7490" title="Upper-Triagle of Choleski decomposition of .">Y</a>.<a class="code" href="classsqmat.html#071e80ced9cc3b8cbb360fa7462eb646" title="Reimplementing common functions of mat: cols().">rows</a>(); <a class="code" href="classbdm_1_1epdf.html#16adac20ec7fe07e1ea0b27d917788ce" title="dimension of the random variable">dim</a> = <a class="code" href="classbdm_1_1eWishartCh.html#b745c73faef785009484180582050a1f" title="dimension of matrix ">p</a>*<a class="code" href="classbdm_1_1eWishartCh.html#b745c73faef785009484180582050a1f" title="dimension of matrix ">p</a>; }
+<a name="l00884"></a>00884                         mat sample_mat()<span class="keyword"> const</span>
+<a name="l00885"></a>00885 <span class="keyword">                        </span>{
+<a name="l00886"></a>00886                                 mat X=zeros ( <a class="code" href="classbdm_1_1eWishartCh.html#b745c73faef785009484180582050a1f" title="dimension of matrix ">p</a>,<a class="code" href="classbdm_1_1eWishartCh.html#b745c73faef785009484180582050a1f" title="dimension of matrix ">p</a> );
+<a name="l00887"></a>00887 
+<a name="l00888"></a>00888                                 <span class="comment">//sample diagonal</span>
+<a name="l00889"></a>00889                                 <span class="keywordflow">for</span> ( <span class="keywordtype">int</span> i=0;i&lt;<a class="code" href="classbdm_1_1eWishartCh.html#b745c73faef785009484180582050a1f" title="dimension of matrix ">p</a>;i++ )
+<a name="l00890"></a>00890                                 {
+<a name="l00891"></a>00891                                         GamRNG.setup ( 0.5* ( <a class="code" href="classbdm_1_1eWishartCh.html#1879a14d7d2bb05062523b189baa11c3" title="degrees of freedom ">delta</a>-i ) , 0.5 ); <span class="comment">// no +1 !! index if from 0</span>
+<a name="l00892"></a>00892 <span class="preprocessor">#pragma omp critical</span>
+<a name="l00893"></a>00893 <span class="preprocessor"></span>                                        X ( i,i ) =sqrt ( GamRNG() );
+<a name="l00894"></a>00894                                 }
+<a name="l00895"></a>00895                                 <span class="comment">//do the rest</span>
+<a name="l00896"></a>00896                                 <span class="keywordflow">for</span> ( <span class="keywordtype">int</span> i=0;i&lt;p;i++ )
+<a name="l00897"></a>00897                                 {
+<a name="l00898"></a>00898                                         <span class="keywordflow">for</span> ( <span class="keywordtype">int</span> j=i+1;j&lt;p;j++ )
+<a name="l00899"></a>00899                                         {
+<a name="l00900"></a>00900 <span class="preprocessor">#pragma omp critical</span>
+<a name="l00901"></a>00901 <span class="preprocessor"></span>                                                X ( i,j ) =NorRNG.sample();
+<a name="l00902"></a>00902                                         }
+<a name="l00903"></a>00903                                 }
+<a name="l00904"></a>00904                                 <span class="keywordflow">return</span> X*<a class="code" href="classbdm_1_1eWishartCh.html#1b42f9284a32f23b0b253a628cda7490" title="Upper-Triagle of Choleski decomposition of .">Y</a>.<a class="code" href="classchmat.html#9c50d31c999d85d8e9d8cf2b69b6ac8c" title="Access function.">_Ch</a>();<span class="comment">// return upper triangular part of the decomposition</span>
+<a name="l00905"></a>00905                         }
+<a name="l00906"></a><a class="code" href="classbdm_1_1eWishartCh.html#8f2154b8b5be8f4c5788f261b6d57b9a">00906</a>                         vec <a class="code" href="classbdm_1_1eWishartCh.html#8f2154b8b5be8f4c5788f261b6d57b9a" title="Returns a sample,  from density .">sample</a> ()<span class="keyword"> const</span>
+<a name="l00907"></a>00907 <span class="keyword">                        </span>{
+<a name="l00908"></a>00908                                 <span class="keywordflow">return</span> vec ( sample_mat()._data(),p*p );
+<a name="l00909"></a>00909                         }
+<a name="l00911"></a><a class="code" href="classbdm_1_1eWishartCh.html#4eee757c0535c2a88bb20f0767c64981">00911</a>                         <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1eWishartCh.html#4eee757c0535c2a88bb20f0767c64981" title="fast access function y0 will be copied into Y.Ch.">setY</a> ( <span class="keyword">const</span> mat &amp;Ch0 ) {copy_vector ( <a class="code" href="classbdm_1_1epdf.html#16adac20ec7fe07e1ea0b27d917788ce" title="dimension of the random variable">dim</a>,Ch0._data(), <a class="code" href="classbdm_1_1eWishartCh.html#1b42f9284a32f23b0b253a628cda7490" title="Upper-Triagle of Choleski decomposition of .">Y</a>.<a class="code" href="classchmat.html#9c50d31c999d85d8e9d8cf2b69b6ac8c" title="Access function.">_Ch</a>()._data() );}
+<a name="l00913"></a><a class="code" href="classbdm_1_1eWishartCh.html#7eac414ec10b85aa5536b0092c57bc4a">00913</a>                         <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1eWishartCh.html#7eac414ec10b85aa5536b0092c57bc4a" title="fast access function y0 will be copied into Y.Ch.">_setY</a> ( <span class="keyword">const</span> vec &amp;ch0 ) {copy_vector ( <a class="code" href="classbdm_1_1epdf.html#16adac20ec7fe07e1ea0b27d917788ce" title="dimension of the random variable">dim</a>, ch0._data(), <a class="code" href="classbdm_1_1eWishartCh.html#1b42f9284a32f23b0b253a628cda7490" title="Upper-Triagle of Choleski decomposition of .">Y</a>.<a class="code" href="classchmat.html#9c50d31c999d85d8e9d8cf2b69b6ac8c" title="Access function.">_Ch</a>()._data() ); }
+<a name="l00915"></a><a class="code" href="classbdm_1_1eWishartCh.html#1708cacb5d8cb1b96395d35f5327cb7e">00915</a>                         <span class="keyword">const</span> <a class="code" href="classchmat.html" title="Symmetric matrix stored in square root decomposition using upper cholesky.">chmat</a>&amp; <a class="code" href="classbdm_1_1eWishartCh.html#1708cacb5d8cb1b96395d35f5327cb7e" title="access function">getY</a>()<span class="keyword">const </span>{<span class="keywordflow">return</span> <a class="code" href="classbdm_1_1eWishartCh.html#1b42f9284a32f23b0b253a628cda7490" title="Upper-Triagle of Choleski decomposition of .">Y</a>;}
+<a name="l00916"></a>00916         };
+<a name="l00917"></a>00917 
+<a name="l00918"></a>00918         <span class="keyword">class </span>eiWishartCh: <span class="keyword">public</span> epdf
+<a name="l00919"></a>00919         {
+<a name="l00920"></a>00920                 <span class="keyword">protected</span>:
+<a name="l00921"></a>00921                         eWishartCh W;
+<a name="l00922"></a>00922                         <span class="keywordtype">int</span> p;
+<a name="l00923"></a>00923                         <span class="keywordtype">double</span> delta;
+<a name="l00924"></a>00924                 <span class="keyword">public</span>:
+<a name="l00925"></a>00925                         <span class="keywordtype">void</span> set_parameters ( <span class="keyword">const</span> mat &amp;Y0, <span class="keyword">const</span> <span class="keywordtype">double</span> delta0) {
+<a name="l00926"></a>00926                                 delta = delta0;
+<a name="l00927"></a>00927                                 W.set_parameters ( inv ( Y0 ),delta0 ); 
+<a name="l00928"></a>00928                                 dim = W.dimension(); p=Y0.rows();
+<a name="l00929"></a>00929                         }
+<a name="l00930"></a>00930                         vec sample()<span class="keyword"> const </span>{mat iCh; iCh=inv ( W.sample_mat() ); <span class="keywordflow">return</span> vec ( iCh._data(),<a class="code" href="classbdm_1_1epdf.html#16adac20ec7fe07e1ea0b27d917788ce" title="dimension of the random variable">dim</a> );}
+<a name="l00931"></a>00931                         <span class="keywordtype">void</span> _setY ( <span class="keyword">const</span> vec &amp;y0 )
+<a name="l00932"></a>00932                         {
+<a name="l00933"></a>00933                                 mat Ch ( p,p );
+<a name="l00934"></a>00934                                 mat iCh ( p,p );
+<a name="l00935"></a>00935                                 copy_vector ( dim, y0._data(), Ch._data() );
+<a name="l00936"></a>00936                                 
+<a name="l00937"></a>00937                                 iCh=inv ( Ch );
+<a name="l00938"></a>00938                                 W.setY ( iCh );
+<a name="l00939"></a>00939                         }
+<a name="l00940"></a>00940                         <span class="keyword">virtual</span> <span class="keywordtype">double</span> evallog ( <span class="keyword">const</span> vec &amp;val )<span class="keyword"> const </span>{
+<a name="l00941"></a>00941                                 <a class="code" href="classchmat.html" title="Symmetric matrix stored in square root decomposition using upper cholesky.">chmat</a> X(p);
+<a name="l00942"></a>00942                                 <span class="keyword">const</span> <a class="code" href="classchmat.html" title="Symmetric matrix stored in square root decomposition using upper cholesky.">chmat</a>&amp; Y=W.getY();
+<a name="l00943"></a>00943                                  
+<a name="l00944"></a>00944                                 copy_vector(p*p,val._data(),X._Ch()._data());
+<a name="l00945"></a>00945                                 <a class="code" href="classchmat.html" title="Symmetric matrix stored in square root decomposition using upper cholesky.">chmat</a> iX(p);X.inv(iX);
+<a name="l00946"></a>00946                                 <span class="comment">// compute  </span>
+<a name="l00947"></a>00947 <span class="comment">//                              \frac{ |\Psi|^{m/2}|X|^{-(m+p+1)/2}e^{-tr(\Psi X^{-1})/2} }{ 2^{mp/2}\Gamma_p(m/2)},</span>
+<a name="l00948"></a>00948                                 mat M=Y.<a class="code" href="classchmat.html#045addd685f8d978efda232d7dcb070e" title="Conversion to full matrix.">to_mat</a>()*iX.to_mat();
+<a name="l00949"></a>00949                                 
+<a name="l00950"></a>00950                                 <span class="keywordtype">double</span> log1 = 0.5*p*(2*Y.<a class="code" href="classchmat.html#b504ca818203b13e667cb3c503980382" title="Logarithm of a determinant.">logdet</a>())-0.5*(delta+p+1)*(2*X.logdet())-0.5*trace(M); 
+<a name="l00951"></a>00951                                 <span class="comment">//Fixme! Multivariate gamma omitted!! it is ok for sampling, but not otherwise!!</span>
+<a name="l00952"></a>00952                                 
+<a name="l00953"></a>00953 <span class="comment">/*                              if (0) {</span>
+<a name="l00954"></a>00954 <span class="comment">                                        mat XX=X.to_mat();</span>
+<a name="l00955"></a>00955 <span class="comment">                                        mat YY=Y.to_mat();</span>
+<a name="l00956"></a>00956 <span class="comment">                                        </span>
+<a name="l00957"></a>00957 <span class="comment">                                        double log2 = 0.5*p*log(det(YY))-0.5*(delta+p+1)*log(det(XX))-0.5*trace(YY*inv(XX)); </span>
+<a name="l00958"></a>00958 <span class="comment">                                        cout &lt;&lt; log1 &lt;&lt; "," &lt;&lt; log2 &lt;&lt; endl;</span>
+<a name="l00959"></a>00959 <span class="comment">                                }*/</span>
+<a name="l00960"></a>00960                                 <span class="keywordflow">return</span> log1;                            
+<a name="l00961"></a>00961                         };
+<a name="l00962"></a>00962                         
+<a name="l00963"></a>00963         };
+<a name="l00964"></a>00964 
+<a name="l00965"></a>00965         <span class="keyword">class </span>rwiWishartCh : <span class="keyword">public</span> mpdf
+<a name="l00966"></a>00966         {
+<a name="l00967"></a>00967                 <span class="keyword">protected</span>:
+<a name="l00968"></a>00968                         eiWishartCh eiW;
+<a name="l00970"></a>00970                         <span class="keywordtype">double</span> sqd;
+<a name="l00971"></a>00971                         <span class="comment">//reference point for diagonal</span>
+<a name="l00972"></a>00972                         vec refl;
+<a name="l00973"></a>00973                         <span class="keywordtype">double</span> l;
+<a name="l00974"></a>00974                         <span class="keywordtype">int</span> p;
+<a name="l00975"></a>00975                 <span class="keyword">public</span>:
+<a name="l00976"></a>00976                         <span class="keywordtype">void</span> set_parameters ( <span class="keywordtype">int</span> p0, <span class="keywordtype">double</span> k, vec ref0, <span class="keywordtype">double</span> l0  )
+<a name="l00977"></a>00977                         {
+<a name="l00978"></a>00978                                 p=p0;
+<a name="l00979"></a>00979                                 <span class="keywordtype">double</span> delta = 2/(k*k)+p+3;
+<a name="l00980"></a>00980                                 sqd=sqrt ( delta-p-1 );
+<a name="l00981"></a>00981                                 l=l0;
+<a name="l00982"></a>00982                                 refl=pow(ref0,1-l);
+<a name="l00983"></a>00983                                 
+<a name="l00984"></a>00984                                 eiW.set_parameters ( eye ( p ),delta );
+<a name="l00985"></a>00985                                 <a class="code" href="classbdm_1_1mpdf.html#5eea43c56d38e4441bfb30270db949c0" title="pointer to internal epdf">ep</a>=&amp;eiW;
+<a name="l00986"></a>00986                                 <a class="code" href="classbdm_1_1mpdf.html#7c1900976ff13dbc09c9729b3bbff9e6" title="dimension of the condition">dimc</a>=eiW.dimension();
+<a name="l00987"></a>00987                         }
+<a name="l00988"></a>00988                         <span class="keywordtype">void</span> condition ( <span class="keyword">const</span> vec &amp;c ) {
+<a name="l00989"></a>00989                                 vec z=c;
+<a name="l00990"></a>00990                                 <span class="keywordtype">int</span> ri=0;
+<a name="l00991"></a>00991                                 <span class="keywordflow">for</span>(<span class="keywordtype">int</span> i=0;i&lt;p*p;i+=(p+1)){<span class="comment">//trace diagonal element</span>
+<a name="l00992"></a>00992                                         z(i) = pow(z(i),l)*refl(ri);
+<a name="l00993"></a>00993                                         ri++;
+<a name="l00994"></a>00994                                 }
+<a name="l00995"></a>00995 
+<a name="l00996"></a>00996                                 eiW._setY ( sqd*z );
+<a name="l00997"></a>00997                         }
+<a name="l00998"></a>00998         };
+<a name="l00999"></a>00999 
+<a name="l01001"></a>01001         <span class="keyword">enum</span> RESAMPLING_METHOD { MULTINOMIAL = 0, STRATIFIED = 1, SYSTEMATIC = 3 };
+<a name="l01007"></a><a class="code" href="classbdm_1_1eEmp.html">01007</a>         <span class="keyword">class </span><a class="code" href="classbdm_1_1eEmp.html" title="Weighted empirical density.">eEmp</a>: <span class="keyword">public</span> <a class="code" href="classbdm_1_1epdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a>
+<a name="l01008"></a>01008         {
+<a name="l01009"></a>01009                 <span class="keyword">protected</span> :
+<a name="l01011"></a><a class="code" href="classbdm_1_1eEmp.html#9798006271ca77629855113f1283a031">01011</a>                         <span class="keywordtype">int</span> <a class="code" href="classbdm_1_1eEmp.html#9798006271ca77629855113f1283a031" title="Number of particles.">n</a>;
+<a name="l01013"></a><a class="code" href="classbdm_1_1eEmp.html#9d39241aab7c4bbeb07c6d516421c67d">01013</a>                         vec <a class="code" href="classbdm_1_1eEmp.html#9d39241aab7c4bbeb07c6d516421c67d" title="Sample weights .">w</a>;
+<a name="l01015"></a><a class="code" href="classbdm_1_1eEmp.html#73d819553a0f268b055a087d2d4486f3">01015</a>                         Array&lt;vec&gt; <a class="code" href="classbdm_1_1eEmp.html#73d819553a0f268b055a087d2d4486f3" title="Samples .">samples</a>;
+<a name="l01016"></a>01016                 <span class="keyword">public</span>:
+<a name="l01019"></a>01019                         <a class="code" href="classbdm_1_1eEmp.html" title="Weighted empirical density.">eEmp</a> ( ) :<a class="code" href="classbdm_1_1epdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a> ( ),<a class="code" href="classbdm_1_1eEmp.html#9d39241aab7c4bbeb07c6d516421c67d" title="Sample weights .">w</a> ( ),<a class="code" href="classbdm_1_1eEmp.html#73d819553a0f268b055a087d2d4486f3" title="Samples .">samples</a> ( ) {};
+<a name="l01021"></a><a class="code" href="classbdm_1_1eEmp.html#a3daf6363455af099921715e1233c076">01021</a>                         <a class="code" href="classbdm_1_1eEmp.html#a3daf6363455af099921715e1233c076" title="copy constructor">eEmp</a> ( <span class="keyword">const</span> <a class="code" href="classbdm_1_1eEmp.html" title="Weighted empirical density.">eEmp</a> &amp;e ) : <a class="code" href="classbdm_1_1epdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a> ( e ), <a class="code" href="classbdm_1_1eEmp.html#9d39241aab7c4bbeb07c6d516421c67d" title="Sample weights .">w</a> ( e.<a class="code" href="classbdm_1_1eEmp.html#9d39241aab7c4bbeb07c6d516421c67d" title="Sample weights .">w</a> ), <a class="code" href="classbdm_1_1eEmp.html#73d819553a0f268b055a087d2d4486f3" title="Samples .">samples</a> ( e.<a class="code" href="classbdm_1_1eEmp.html#73d819553a0f268b055a087d2d4486f3" title="Samples .">samples</a> ) {};
+<a name="l01023"></a>01023 
+<a name="l01025"></a>01025                         <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1eEmp.html#7cfd383180b486fe4526bdf0179350c0" title="Set samples and weights.">set_statistics</a> ( <span class="keyword">const</span> vec &amp;w0, <span class="keyword">const</span> <a class="code" href="classbdm_1_1epdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a>* pdf0 );
+<a name="l01027"></a><a class="code" href="classbdm_1_1eEmp.html#cef74aa5f87d10d440b9b1e8bc78c1e7">01027</a>                         <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1eEmp.html#cef74aa5f87d10d440b9b1e8bc78c1e7" title="Set samples and weights.">set_statistics</a> ( <span class="keyword">const</span> <a class="code" href="classbdm_1_1epdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a>* pdf0 , <span class="keywordtype">int</span> <a class="code" href="classbdm_1_1eEmp.html#9798006271ca77629855113f1283a031" title="Number of particles.">n</a> ) {<a class="code" href="classbdm_1_1eEmp.html#cef74aa5f87d10d440b9b1e8bc78c1e7" title="Set samples and weights.">set_statistics</a> ( ones ( n ) /n,pdf0 );};
+<a name="l01029"></a>01029                         <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1eEmp.html#b62d802b8ef39f7c4dcbeb366c90951a" title="Set sample.">set_samples</a> ( <span class="keyword">const</span> <a class="code" href="classbdm_1_1epdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a>* pdf0 );
+<a name="l01031"></a><a class="code" href="classbdm_1_1eEmp.html#c74c281d652356c19b6b079e42ca7ef1">01031</a>                         <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1eEmp.html#c74c281d652356c19b6b079e42ca7ef1" title="Set sample.">set_parameters</a> ( <span class="keywordtype">int</span> n0, <span class="keywordtype">bool</span> copy=<span class="keyword">true</span> ) {<a class="code" href="classbdm_1_1eEmp.html#9798006271ca77629855113f1283a031" title="Number of particles.">n</a>=n0; <a class="code" href="classbdm_1_1eEmp.html#9d39241aab7c4bbeb07c6d516421c67d" title="Sample weights .">w</a>.set_size ( n0,copy );<a class="code" href="classbdm_1_1eEmp.html#73d819553a0f268b055a087d2d4486f3" title="Samples .">samples</a>.set_size ( n0,copy );};
+<a name="l01033"></a><a class="code" href="classbdm_1_1eEmp.html#d7f83cc0415cd44ae7cc8b4bdad93aef">01033</a>                         vec&amp; <a class="code" href="classbdm_1_1eEmp.html#d7f83cc0415cd44ae7cc8b4bdad93aef" title="Potentially dangerous, use with care.">_w</a>()  {<span class="keywordflow">return</span> <a class="code" href="classbdm_1_1eEmp.html#9d39241aab7c4bbeb07c6d516421c67d" title="Sample weights .">w</a>;};
+<a name="l01035"></a><a class="code" href="classbdm_1_1eEmp.html#b7d7106f486e3fad38590914a693d714">01035</a>                         <span class="keyword">const</span> vec&amp; <a class="code" href="classbdm_1_1eEmp.html#b7d7106f486e3fad38590914a693d714" title="Potentially dangerous, use with care.">_w</a>()<span class="keyword"> const </span>{<span class="keywordflow">return</span> <a class="code" href="classbdm_1_1eEmp.html#9d39241aab7c4bbeb07c6d516421c67d" title="Sample weights .">w</a>;};
+<a name="l01037"></a><a class="code" href="classbdm_1_1eEmp.html#c24966b0aaeb767bc8a6b4fd60931be2">01037</a>                         Array&lt;vec&gt;&amp; <a class="code" href="classbdm_1_1eEmp.html#c24966b0aaeb767bc8a6b4fd60931be2" title="access function">_samples</a>() {<span class="keywordflow">return</span> <a class="code" href="classbdm_1_1eEmp.html#73d819553a0f268b055a087d2d4486f3" title="Samples .">samples</a>;};
+<a name="l01039"></a><a class="code" href="classbdm_1_1eEmp.html#b59af0efdb009d98ea8ebfa965e74ae2">01039</a>                         <span class="keyword">const</span> Array&lt;vec&gt;&amp; <a class="code" href="classbdm_1_1eEmp.html#b59af0efdb009d98ea8ebfa965e74ae2" title="access function">_samples</a>()<span class="keyword"> const </span>{<span class="keywordflow">return</span> <a class="code" href="classbdm_1_1eEmp.html#73d819553a0f268b055a087d2d4486f3" title="Samples .">samples</a>;};
+<a name="l01041"></a>01041                         ivec <a class="code" href="classbdm_1_1eEmp.html#f06ce255de5dbb2313f52ee51f82ba3d" title="Function performs resampling, i.e. removal of low-weight samples and duplication...">resample</a> ( RESAMPLING_METHOD method=SYSTEMATIC );
+<a name="l01043"></a><a class="code" href="classbdm_1_1eEmp.html#97f1e07b5ae6eebc91c7365f0f88d270">01043</a>                         vec <a class="code" href="classbdm_1_1eEmp.html#97f1e07b5ae6eebc91c7365f0f88d270" title="inherited operation : NOT implemneted">sample</a>()<span class="keyword"> const </span>{it_error ( <span class="stringliteral">"Not implemented"</span> );<span class="keywordflow">return</span> 0;}
+<a name="l01045"></a><a class="code" href="classbdm_1_1eEmp.html#01654c014d3aa068f8d4ecba4be86d09">01045</a>                         <span class="keywordtype">double</span> <a class="code" href="classbdm_1_1eEmp.html#01654c014d3aa068f8d4ecba4be86d09" title="inherited operation : NOT implemneted">evallog</a> ( <span class="keyword">const</span> vec &amp;val )<span class="keyword"> const </span>{it_error ( <span class="stringliteral">"Not implemented"</span> );<span class="keywordflow">return</span> 0.0;}
+<a name="l01046"></a><a class="code" href="classbdm_1_1eEmp.html#bbfcb4f868c7381298c281a256d8c4b9">01046</a>                         vec <a class="code" href="classbdm_1_1eEmp.html#bbfcb4f868c7381298c281a256d8c4b9" title="return expected value">mean</a>()<span class="keyword"> const</span>
+<a name="l01047"></a>01047 <span class="keyword">                        </span>{
+<a name="l01048"></a>01048                                 vec pom=zeros ( <a class="code" href="classbdm_1_1epdf.html#16adac20ec7fe07e1ea0b27d917788ce" title="dimension of the random variable">dim</a> );
+<a name="l01049"></a>01049                                 <span class="keywordflow">for</span> ( <span class="keywordtype">int</span> i=0;i&lt;<a class="code" href="classbdm_1_1eEmp.html#9798006271ca77629855113f1283a031" title="Number of particles.">n</a>;i++ ) {pom+=<a class="code" href="classbdm_1_1eEmp.html#73d819553a0f268b055a087d2d4486f3" title="Samples .">samples</a> ( i ) *<a class="code" href="classbdm_1_1eEmp.html#9d39241aab7c4bbeb07c6d516421c67d" title="Sample weights .">w</a> ( i );}
+<a name="l01050"></a>01050                                 <span class="keywordflow">return</span> pom;
+<a name="l01051"></a>01051                         }
+<a name="l01052"></a><a class="code" href="classbdm_1_1eEmp.html#05e9ebf467ede737cb6a3621d7fd3c87">01052</a>                         vec <a class="code" href="classbdm_1_1eEmp.html#05e9ebf467ede737cb6a3621d7fd3c87" title="return expected variance (not covariance!)">variance</a>()<span class="keyword"> const</span>
+<a name="l01053"></a>01053 <span class="keyword">                        </span>{
+<a name="l01054"></a>01054                                 vec pom=zeros ( <a class="code" href="classbdm_1_1epdf.html#16adac20ec7fe07e1ea0b27d917788ce" title="dimension of the random variable">dim</a> );
+<a name="l01055"></a>01055                                 <span class="keywordflow">for</span> ( <span class="keywordtype">int</span> i=0;i&lt;<a class="code" href="classbdm_1_1eEmp.html#9798006271ca77629855113f1283a031" title="Number of particles.">n</a>;i++ ) {pom+=pow ( <a class="code" href="classbdm_1_1eEmp.html#73d819553a0f268b055a087d2d4486f3" title="Samples .">samples</a> ( i ),2 ) *<a class="code" href="classbdm_1_1eEmp.html#9d39241aab7c4bbeb07c6d516421c67d" title="Sample weights .">w</a> ( i );}
+<a name="l01056"></a>01056                                 <span class="keywordflow">return</span> pom-pow ( <a class="code" href="classbdm_1_1eEmp.html#bbfcb4f868c7381298c281a256d8c4b9" title="return expected value">mean</a>(),2 );
+<a name="l01057"></a>01057                         }
+<a name="l01059"></a><a class="code" href="classbdm_1_1eEmp.html#b1c9df656144edf79ba2d885613f661f">01059</a>                         <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1eEmp.html#b1c9df656144edf79ba2d885613f661f" title="For this class, qbounds are minimum and maximum value of the population!">qbounds</a> ( vec &amp;lb, vec &amp;ub, <span class="keywordtype">double</span> perc=0.95 )<span class="keyword"> const</span>
+<a name="l01060"></a>01060 <span class="keyword">                        </span>{
+<a name="l01061"></a>01061                                 <span class="comment">// lb in inf so than it will be pushed below;</span>
+<a name="l01062"></a>01062                                 lb.set_size ( <a class="code" href="classbdm_1_1epdf.html#16adac20ec7fe07e1ea0b27d917788ce" title="dimension of the random variable">dim</a> );
+<a name="l01063"></a>01063                                 ub.set_size ( <a class="code" href="classbdm_1_1epdf.html#16adac20ec7fe07e1ea0b27d917788ce" title="dimension of the random variable">dim</a> );
+<a name="l01064"></a>01064                                 lb = std::numeric_limits&lt;double&gt;::infinity();
+<a name="l01065"></a>01065                                 ub = -std::numeric_limits&lt;double&gt;::infinity();
+<a name="l01066"></a>01066                                 <span class="keywordtype">int</span> j;
+<a name="l01067"></a>01067                                 <span class="keywordflow">for</span> ( <span class="keywordtype">int</span> i=0;i&lt;<a class="code" href="classbdm_1_1eEmp.html#9798006271ca77629855113f1283a031" title="Number of particles.">n</a>;i++ )
+<a name="l01068"></a>01068                                 {
+<a name="l01069"></a>01069                                         <span class="keywordflow">for</span> ( j=0;j&lt;<a class="code" href="classbdm_1_1epdf.html#16adac20ec7fe07e1ea0b27d917788ce" title="dimension of the random variable">dim</a>; j++ )
+<a name="l01070"></a>01070                                         {
+<a name="l01071"></a>01071                                                 <span class="keywordflow">if</span> ( <a class="code" href="classbdm_1_1eEmp.html#73d819553a0f268b055a087d2d4486f3" title="Samples .">samples</a> ( i ) ( j ) &lt;lb ( j ) ) {lb ( j ) =<a class="code" href="classbdm_1_1eEmp.html#73d819553a0f268b055a087d2d4486f3" title="Samples .">samples</a> ( i ) ( j );}
+<a name="l01072"></a>01072                                                 <span class="keywordflow">if</span> ( <a class="code" href="classbdm_1_1eEmp.html#73d819553a0f268b055a087d2d4486f3" title="Samples .">samples</a> ( i ) ( j ) &gt;ub ( j ) ) {ub ( j ) =<a class="code" href="classbdm_1_1eEmp.html#73d819553a0f268b055a087d2d4486f3" title="Samples .">samples</a> ( i ) ( j );}
+<a name="l01073"></a>01073                                         }
+<a name="l01074"></a>01074                                 }
+<a name="l01075"></a>01075                         }
+<a name="l01076"></a>01076         };
+<a name="l01077"></a>01077 
 <a name="l01078"></a>01078 
-<a name="l01079"></a>01079         <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt;
-<a name="l01080"></a>01080         <span class="keywordtype">void</span> enorm&lt;sq_T&gt;::set_parameters ( <span class="keyword">const</span> vec &amp;mu0, <span class="keyword">const</span> sq_T &amp;R0 )
-<a name="l01081"></a>01081         {
-<a name="l01082"></a>01082 <span class="comment">//Fixme test dimensions of mu0 and R0;</span>
-<a name="l01083"></a>01083                 mu = mu0;
-<a name="l01084"></a>01084                 R = R0;
-<a name="l01085"></a>01085                 <a class="code" href="classbdm_1_1epdf.html#16adac20ec7fe07e1ea0b27d917788ce" title="dimension of the random variable">dim</a> = mu0.length();
-<a name="l01086"></a>01086         };
-<a name="l01087"></a>01087 
-<a name="l01088"></a>01088         <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt;
-<a name="l01089"></a><a class="code" href="classbdm_1_1enorm.html#d2e0d3a1e30ab3ab04df2d0c43ae74a2">01089</a>         <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1enorm.html#d2e0d3a1e30ab3ab04df2d0c43ae74a2" title="dupdate in exponential form (not really handy)">enorm&lt;sq_T&gt;::dupdate</a> ( mat &amp;v, <span class="keywordtype">double</span> nu )
-<a name="l01090"></a>01090         {
-<a name="l01091"></a>01091                 <span class="comment">//</span>
-<a name="l01092"></a>01092         };
-<a name="l01093"></a>01093 
-<a name="l01094"></a>01094 <span class="comment">// template&lt;class sq_T&gt;</span>
-<a name="l01095"></a>01095 <span class="comment">// void enorm&lt;sq_T&gt;::tupdate ( double phi, mat &amp;vbar, double nubar ) {</span>
-<a name="l01096"></a>01096 <span class="comment">//      //</span>
-<a name="l01097"></a>01097 <span class="comment">// };</span>
-<a name="l01098"></a>01098 
-<a name="l01099"></a>01099         <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt;
-<a name="l01100"></a><a class="code" href="classbdm_1_1enorm.html#e1a48f52351ec3a349bd443b713b1766">01100</a>         vec <a class="code" href="classbdm_1_1enorm.html#e1a48f52351ec3a349bd443b713b1766" title="Returns a sample,  from density .">enorm&lt;sq_T&gt;::sample</a>()<span class="keyword"> const</span>
-<a name="l01101"></a>01101 <span class="keyword">        </span>{
-<a name="l01102"></a>01102                 vec x ( <a class="code" href="classbdm_1_1epdf.html#16adac20ec7fe07e1ea0b27d917788ce" title="dimension of the random variable">dim</a> );
-<a name="l01103"></a>01103 <span class="preprocessor">#pragma omp critical</span>
-<a name="l01104"></a>01104 <span class="preprocessor"></span>                NorRNG.sample_vector ( <a class="code" href="classbdm_1_1epdf.html#16adac20ec7fe07e1ea0b27d917788ce" title="dimension of the random variable">dim</a>,x );
-<a name="l01105"></a>01105                 vec smp = <a class="code" href="classbdm_1_1enorm.html#2d92dde696b2a7a5b10ddef5d22ba2c2" title="Covariance matrix in decomposed form.">R</a>.sqrt_mult ( x );
-<a name="l01106"></a>01106 
-<a name="l01107"></a>01107                 smp += <a class="code" href="classbdm_1_1enorm.html#c702a194720853570d08b65482f842c7" title="mean value">mu</a>;
-<a name="l01108"></a>01108                 <span class="keywordflow">return</span> smp;
-<a name="l01109"></a>01109         };
-<a name="l01110"></a>01110 
-<a name="l01111"></a>01111         <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt;
-<a name="l01112"></a>01112         mat <a class="code" href="classbdm_1_1enorm.html#e1a48f52351ec3a349bd443b713b1766" title="Returns a sample,  from density .">enorm&lt;sq_T&gt;::sample</a> ( <span class="keywordtype">int</span> N )<span class="keyword"> const</span>
-<a name="l01113"></a>01113 <span class="keyword">        </span>{
-<a name="l01114"></a>01114                 mat X ( <a class="code" href="classbdm_1_1epdf.html#16adac20ec7fe07e1ea0b27d917788ce" title="dimension of the random variable">dim</a>,N );
-<a name="l01115"></a>01115                 vec x ( <a class="code" href="classbdm_1_1epdf.html#16adac20ec7fe07e1ea0b27d917788ce" title="dimension of the random variable">dim</a> );
-<a name="l01116"></a>01116                 vec pom;
-<a name="l01117"></a>01117                 <span class="keywordtype">int</span> i;
-<a name="l01118"></a>01118 
-<a name="l01119"></a>01119                 <span class="keywordflow">for</span> ( i=0;i&lt;N;i++ )
-<a name="l01120"></a>01120                 {
-<a name="l01121"></a>01121 <span class="preprocessor">#pragma omp critical</span>
-<a name="l01122"></a>01122 <span class="preprocessor"></span>                        NorRNG.sample_vector ( <a class="code" href="classbdm_1_1epdf.html#16adac20ec7fe07e1ea0b27d917788ce" title="dimension of the random variable">dim</a>,x );
-<a name="l01123"></a>01123                         pom = <a class="code" href="classbdm_1_1enorm.html#2d92dde696b2a7a5b10ddef5d22ba2c2" title="Covariance matrix in decomposed form.">R</a>.sqrt_mult ( x );
-<a name="l01124"></a>01124                         pom +=<a class="code" href="classbdm_1_1enorm.html#c702a194720853570d08b65482f842c7" title="mean value">mu</a>;
-<a name="l01125"></a>01125                         X.set_col ( i, pom );
-<a name="l01126"></a>01126                 }
-<a name="l01127"></a>01127 
-<a name="l01128"></a>01128                 <span class="keywordflow">return</span> X;
-<a name="l01129"></a>01129         };
-<a name="l01130"></a>01130 
-<a name="l01131"></a>01131 <span class="comment">// template&lt;class sq_T&gt;</span>
-<a name="l01132"></a>01132 <span class="comment">// double enorm&lt;sq_T&gt;::eval ( const vec &amp;val ) const {</span>
-<a name="l01133"></a>01133 <span class="comment">//      double pdfl,e;</span>
-<a name="l01134"></a>01134 <span class="comment">//      pdfl = evallog ( val );</span>
-<a name="l01135"></a>01135 <span class="comment">//      e = exp ( pdfl );</span>
-<a name="l01136"></a>01136 <span class="comment">//      return e;</span>
-<a name="l01137"></a>01137 <span class="comment">// };</span>
-<a name="l01138"></a>01138 
-<a name="l01139"></a>01139         <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt;
-<a name="l01140"></a><a class="code" href="classbdm_1_1enorm.html#e13aeed5b543b2179bacdc4fa2ae47a3">01140</a>         <span class="keywordtype">double</span> <a class="code" href="classbdm_1_1enorm.html#e13aeed5b543b2179bacdc4fa2ae47a3" title="Evaluate normalized log-probability.">enorm&lt;sq_T&gt;::evallog_nn</a> ( <span class="keyword">const</span> vec &amp;val )<span class="keyword"> const</span>
-<a name="l01141"></a>01141 <span class="keyword">        </span>{
-<a name="l01142"></a>01142                 <span class="comment">// 1.83787706640935 = log(2pi)</span>
-<a name="l01143"></a>01143                 <span class="keywordtype">double</span> tmp=-0.5* ( <a class="code" href="classbdm_1_1enorm.html#2d92dde696b2a7a5b10ddef5d22ba2c2" title="Covariance matrix in decomposed form.">R</a>.invqform ( <a class="code" href="classbdm_1_1enorm.html#c702a194720853570d08b65482f842c7" title="mean value">mu</a>-val ) );<span class="comment">// - lognc();</span>
-<a name="l01144"></a>01144                 <span class="keywordflow">return</span>  tmp;
-<a name="l01145"></a>01145         };
-<a name="l01146"></a>01146 
-<a name="l01147"></a>01147         <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt;
-<a name="l01148"></a><a class="code" href="classbdm_1_1enorm.html#25785343aff102cc5df1cab08ba16d32">01148</a>         <span class="keyword">inline</span> <span class="keywordtype">double</span> <a class="code" href="classbdm_1_1enorm.html#25785343aff102cc5df1cab08ba16d32" title="logarithm of the normalizing constant, ">enorm&lt;sq_T&gt;::lognc</a> ()<span class="keyword"> const</span>
-<a name="l01149"></a>01149 <span class="keyword">        </span>{
-<a name="l01150"></a>01150                 <span class="comment">// 1.83787706640935 = log(2pi)</span>
-<a name="l01151"></a>01151                 <span class="keywordtype">double</span> tmp=0.5* ( <a class="code" href="classbdm_1_1enorm.html#2d92dde696b2a7a5b10ddef5d22ba2c2" title="Covariance matrix in decomposed form.">R</a>.cols() * 1.83787706640935 +<a class="code" href="classbdm_1_1enorm.html#2d92dde696b2a7a5b10ddef5d22ba2c2" title="Covariance matrix in decomposed form.">R</a>.logdet() );
-<a name="l01152"></a>01152                 <span class="keywordflow">return</span> tmp;
-<a name="l01153"></a>01153         };
-<a name="l01154"></a>01154 
-<a name="l01155"></a>01155         <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt;
-<a name="l01156"></a><a class="code" href="classbdm_1_1mlnorm.html#5d18dec3167584338a4775c1d165d96f">01156</a>         <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1mlnorm.html#5d18dec3167584338a4775c1d165d96f" title="Set A and R.">mlnorm&lt;sq_T&gt;::set_parameters</a> ( <span class="keyword">const</span> mat &amp;A0, <span class="keyword">const</span> vec &amp;mu0, <span class="keyword">const</span> sq_T &amp;R0 )
-<a name="l01157"></a>01157         {
-<a name="l01158"></a>01158                 it_assert_debug ( A0.rows() ==mu0.length(),<span class="stringliteral">""</span> );
-<a name="l01159"></a>01159                 it_assert_debug ( A0.rows() ==R0.rows(),<span class="stringliteral">""</span> );
-<a name="l01160"></a>01160 
-<a name="l01161"></a>01161                 <a class="code" href="classbdm_1_1epdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a>.set_parameters ( zeros ( A0.rows() ),R0 );
-<a name="l01162"></a>01162                 A = A0;
-<a name="l01163"></a>01163                 mu_const = mu0;
-<a name="l01164"></a>01164                 <a class="code" href="classbdm_1_1mpdf.html#7c1900976ff13dbc09c9729b3bbff9e6" title="dimension of the condition">dimc</a>=A0.cols();
-<a name="l01165"></a>01165         }
-<a name="l01166"></a>01166 
-<a name="l01167"></a>01167 <span class="comment">// template&lt;class sq_T&gt;</span>
-<a name="l01168"></a>01168 <span class="comment">// vec mlnorm&lt;sq_T&gt;::samplecond (const  vec &amp;cond, double &amp;lik ) {</span>
-<a name="l01169"></a>01169 <span class="comment">//      this-&gt;condition ( cond );</span>
-<a name="l01170"></a>01170 <span class="comment">//      vec smp = epdf.sample();</span>
-<a name="l01171"></a>01171 <span class="comment">//      lik = epdf.eval ( smp );</span>
-<a name="l01172"></a>01172 <span class="comment">//      return smp;</span>
-<a name="l01173"></a>01173 <span class="comment">// }</span>
-<a name="l01174"></a>01174 
-<a name="l01175"></a>01175 <span class="comment">// template&lt;class sq_T&gt;</span>
-<a name="l01176"></a>01176 <span class="comment">// mat mlnorm&lt;sq_T&gt;::samplecond (const vec &amp;cond, vec &amp;lik, int n ) {</span>
-<a name="l01177"></a>01177 <span class="comment">//      int i;</span>
-<a name="l01178"></a>01178 <span class="comment">//      int dim = rv.count();</span>
-<a name="l01179"></a>01179 <span class="comment">//      mat Smp ( dim,n );</span>
-<a name="l01180"></a>01180 <span class="comment">//      vec smp ( dim );</span>
-<a name="l01181"></a>01181 <span class="comment">//      this-&gt;condition ( cond );</span>
-<a name="l01182"></a>01182 <span class="comment">//</span>
-<a name="l01183"></a>01183 <span class="comment">//      for ( i=0; i&lt;n; i++ ) {</span>
-<a name="l01184"></a>01184 <span class="comment">//              smp = epdf.sample();</span>
-<a name="l01185"></a>01185 <span class="comment">//              lik ( i ) = epdf.eval ( smp );</span>
-<a name="l01186"></a>01186 <span class="comment">//              Smp.set_col ( i ,smp );</span>
-<a name="l01187"></a>01187 <span class="comment">//      }</span>
-<a name="l01188"></a>01188 <span class="comment">//</span>
-<a name="l01189"></a>01189 <span class="comment">//      return Smp;</span>
-<a name="l01190"></a>01190 <span class="comment">// }</span>
-<a name="l01191"></a>01191 
-<a name="l01192"></a>01192         <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt;
-<a name="l01193"></a><a class="code" href="classbdm_1_1mlnorm.html#0dafc0196e7e07fd06dc6716e0e18bc2">01193</a>         <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1mlnorm.html#0dafc0196e7e07fd06dc6716e0e18bc2">mlnorm&lt;sq_T&gt;::condition</a> ( <span class="keyword">const</span> vec &amp;cond )
-<a name="l01194"></a>01194         {
-<a name="l01195"></a>01195                 _mu = A*cond + mu_const;
-<a name="l01196"></a>01196 <span class="comment">//R is already assigned;</span>
-<a name="l01197"></a>01197         }
-<a name="l01198"></a>01198 
-<a name="l01199"></a>01199         <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt;
-<a name="l01200"></a><a class="code" href="classbdm_1_1enorm.html#c2996bdaffad38fbe0fc776db54c9d80">01200</a>         <a class="code" href="classbdm_1_1enorm.html" title="Gaussian density with positive definite (decomposed) covariance matrix.">enorm&lt;sq_T&gt;</a>* <a class="code" href="classbdm_1_1enorm.html#c2996bdaffad38fbe0fc776db54c9d80" title="Return marginal density on the given RV, the remainig rvs are intergrated out.">enorm&lt;sq_T&gt;::marginal</a> ( <span class="keyword">const</span> <a class="code" href="classbdm_1_1RV.html" title="Class representing variables, most often random variables.">RV</a> &amp;rvn )<span class="keyword"> const</span>
-<a name="l01201"></a>01201 <span class="keyword">        </span>{
-<a name="l01202"></a>01202                 it_assert_debug ( <a class="code" href="classbdm_1_1epdf.html#c4b863ff84c7a4882fb3ad18556027f9" title="True if rv is assigned.">isnamed</a>(), <span class="stringliteral">"rv description is not assigned"</span> );
-<a name="l01203"></a>01203                 ivec irvn = rvn.<a class="code" href="classbdm_1_1RV.html#cbebdb5e0d30101a6eb63550ef701c55">dataind</a> ( <a class="code" href="classbdm_1_1epdf.html#62c5b8ff71d9ebe6cd58d3c342eb1dc8" title="Description of the random variable.">rv</a> );
-<a name="l01204"></a>01204 
-<a name="l01205"></a>01205                 sq_T Rn ( <a class="code" href="classbdm_1_1enorm.html#2d92dde696b2a7a5b10ddef5d22ba2c2" title="Covariance matrix in decomposed form.">R</a>,irvn ); <span class="comment">//select rows and columns of R</span>
+<a name="l01080"></a>01080 
+<a name="l01081"></a>01081         <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt;
+<a name="l01082"></a>01082         <span class="keywordtype">void</span> enorm&lt;sq_T&gt;::set_parameters ( <span class="keyword">const</span> vec &amp;mu0, <span class="keyword">const</span> sq_T &amp;R0 )
+<a name="l01083"></a>01083         {
+<a name="l01084"></a>01084 <span class="comment">//Fixme test dimensions of mu0 and R0;</span>
+<a name="l01085"></a>01085                 mu = mu0;
+<a name="l01086"></a>01086                 R = R0;
+<a name="l01087"></a>01087                 <a class="code" href="classbdm_1_1epdf.html#16adac20ec7fe07e1ea0b27d917788ce" title="dimension of the random variable">dim</a> = mu0.length();
+<a name="l01088"></a>01088         };
+<a name="l01089"></a>01089 
+<a name="l01090"></a>01090         <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt;
+<a name="l01091"></a><a class="code" href="classbdm_1_1enorm.html#61bd470764020bea6e1ed35000f259e6">01091</a>         <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1enorm.html#61bd470764020bea6e1ed35000f259e6" title="This method arrange instance properties according the data stored in the Setting...">enorm&lt;sq_T&gt;::from_setting</a>(<span class="keyword">const</span> Setting &amp;root){
+<a name="l01092"></a>01092                 vec <a class="code" href="classbdm_1_1enorm.html#c702a194720853570d08b65482f842c7" title="mean value">mu</a>;
+<a name="l01093"></a>01093                 <a class="code" href="classbdm_1_1UI.html#652bfd23f5052e4f1cb317057d74a3e2" title="This methods tries to build a new double matrix.">UI::get</a>(mu,root,<span class="stringliteral">"mu"</span>);
+<a name="l01094"></a>01094                 mat <a class="code" href="classbdm_1_1enorm.html#2d92dde696b2a7a5b10ddef5d22ba2c2" title="Covariance matrix in decomposed form.">R</a>;
+<a name="l01095"></a>01095                 <a class="code" href="classbdm_1_1UI.html#652bfd23f5052e4f1cb317057d74a3e2" title="This methods tries to build a new double matrix.">UI::get</a>(R,root,<span class="stringliteral">"R"</span>);
+<a name="l01096"></a>01096                 set_parameters(mu,R);
+<a name="l01097"></a>01097                 
+<a name="l01098"></a>01098                 <a class="code" href="classbdm_1_1RV.html" title="Class representing variables, most often random variables.">RV</a>* r = UI::build&lt;RV&gt;(root,<span class="stringliteral">"rv"</span>);
+<a name="l01099"></a>01099                 <a class="code" href="classbdm_1_1epdf.html#f423e28448dbb69ef4905295ec8de8ff" title="Name its rv.">set_rv</a>(*r); 
+<a name="l01100"></a>01100                 <span class="keyword">delete</span> r;
+<a name="l01101"></a>01101         }
+<a name="l01102"></a>01102 
+<a name="l01103"></a>01103         <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt;
+<a name="l01104"></a><a class="code" href="classbdm_1_1enorm.html#d2e0d3a1e30ab3ab04df2d0c43ae74a2">01104</a>         <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1enorm.html#d2e0d3a1e30ab3ab04df2d0c43ae74a2" title="dupdate in exponential form (not really handy)">enorm&lt;sq_T&gt;::dupdate</a> ( mat &amp;v, <span class="keywordtype">double</span> nu )
+<a name="l01105"></a>01105         {
+<a name="l01106"></a>01106                 <span class="comment">//</span>
+<a name="l01107"></a>01107         };
+<a name="l01108"></a>01108 
+<a name="l01109"></a>01109 <span class="comment">// template&lt;class sq_T&gt;</span>
+<a name="l01110"></a>01110 <span class="comment">// void enorm&lt;sq_T&gt;::tupdate ( double phi, mat &amp;vbar, double nubar ) {</span>
+<a name="l01111"></a>01111 <span class="comment">//      //</span>
+<a name="l01112"></a>01112 <span class="comment">// };</span>
+<a name="l01113"></a>01113 
+<a name="l01114"></a>01114         <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt;
+<a name="l01115"></a><a class="code" href="classbdm_1_1enorm.html#e1a48f52351ec3a349bd443b713b1766">01115</a>         vec <a class="code" href="classbdm_1_1enorm.html#e1a48f52351ec3a349bd443b713b1766" title="Returns a sample,  from density .">enorm&lt;sq_T&gt;::sample</a>()<span class="keyword"> const</span>
+<a name="l01116"></a>01116 <span class="keyword">        </span>{
+<a name="l01117"></a>01117                 vec x ( <a class="code" href="classbdm_1_1epdf.html#16adac20ec7fe07e1ea0b27d917788ce" title="dimension of the random variable">dim</a> );
+<a name="l01118"></a>01118 <span class="preprocessor">#pragma omp critical</span>
+<a name="l01119"></a>01119 <span class="preprocessor"></span>                NorRNG.sample_vector ( <a class="code" href="classbdm_1_1epdf.html#16adac20ec7fe07e1ea0b27d917788ce" title="dimension of the random variable">dim</a>,x );
+<a name="l01120"></a>01120                 vec smp = <a class="code" href="classbdm_1_1enorm.html#2d92dde696b2a7a5b10ddef5d22ba2c2" title="Covariance matrix in decomposed form.">R</a>.sqrt_mult ( x );
+<a name="l01121"></a>01121 
+<a name="l01122"></a>01122                 smp += <a class="code" href="classbdm_1_1enorm.html#c702a194720853570d08b65482f842c7" title="mean value">mu</a>;
+<a name="l01123"></a>01123                 <span class="keywordflow">return</span> smp;
+<a name="l01124"></a>01124         };
+<a name="l01125"></a>01125 
+<a name="l01126"></a>01126         <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt;
+<a name="l01127"></a>01127         mat <a class="code" href="classbdm_1_1enorm.html#e1a48f52351ec3a349bd443b713b1766" title="Returns a sample,  from density .">enorm&lt;sq_T&gt;::sample</a> ( <span class="keywordtype">int</span> N )<span class="keyword"> const</span>
+<a name="l01128"></a>01128 <span class="keyword">        </span>{
+<a name="l01129"></a>01129                 mat X ( <a class="code" href="classbdm_1_1epdf.html#16adac20ec7fe07e1ea0b27d917788ce" title="dimension of the random variable">dim</a>,N );
+<a name="l01130"></a>01130                 vec x ( <a class="code" href="classbdm_1_1epdf.html#16adac20ec7fe07e1ea0b27d917788ce" title="dimension of the random variable">dim</a> );
+<a name="l01131"></a>01131                 vec pom;
+<a name="l01132"></a>01132                 <span class="keywordtype">int</span> i;
+<a name="l01133"></a>01133 
+<a name="l01134"></a>01134                 <span class="keywordflow">for</span> ( i=0;i&lt;N;i++ )
+<a name="l01135"></a>01135                 {
+<a name="l01136"></a>01136 <span class="preprocessor">#pragma omp critical</span>
+<a name="l01137"></a>01137 <span class="preprocessor"></span>                        NorRNG.sample_vector ( <a class="code" href="classbdm_1_1epdf.html#16adac20ec7fe07e1ea0b27d917788ce" title="dimension of the random variable">dim</a>,x );
+<a name="l01138"></a>01138                         pom = <a class="code" href="classbdm_1_1enorm.html#2d92dde696b2a7a5b10ddef5d22ba2c2" title="Covariance matrix in decomposed form.">R</a>.sqrt_mult ( x );
+<a name="l01139"></a>01139                         pom +=<a class="code" href="classbdm_1_1enorm.html#c702a194720853570d08b65482f842c7" title="mean value">mu</a>;
+<a name="l01140"></a>01140                         X.set_col ( i, pom );
+<a name="l01141"></a>01141                 }
+<a name="l01142"></a>01142 
+<a name="l01143"></a>01143                 <span class="keywordflow">return</span> X;
+<a name="l01144"></a>01144         };
+<a name="l01145"></a>01145 
+<a name="l01146"></a>01146 <span class="comment">// template&lt;class sq_T&gt;</span>
+<a name="l01147"></a>01147 <span class="comment">// double enorm&lt;sq_T&gt;::eval ( const vec &amp;val ) const {</span>
+<a name="l01148"></a>01148 <span class="comment">//      double pdfl,e;</span>
+<a name="l01149"></a>01149 <span class="comment">//      pdfl = evallog ( val );</span>
+<a name="l01150"></a>01150 <span class="comment">//      e = exp ( pdfl );</span>
+<a name="l01151"></a>01151 <span class="comment">//      return e;</span>
+<a name="l01152"></a>01152 <span class="comment">// };</span>
+<a name="l01153"></a>01153 
+<a name="l01154"></a>01154         <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt;
+<a name="l01155"></a><a class="code" href="classbdm_1_1enorm.html#e13aeed5b543b2179bacdc4fa2ae47a3">01155</a>         <span class="keywordtype">double</span> <a class="code" href="classbdm_1_1enorm.html#e13aeed5b543b2179bacdc4fa2ae47a3" title="Evaluate normalized log-probability.">enorm&lt;sq_T&gt;::evallog_nn</a> ( <span class="keyword">const</span> vec &amp;val )<span class="keyword"> const</span>
+<a name="l01156"></a>01156 <span class="keyword">        </span>{
+<a name="l01157"></a>01157                 <span class="comment">// 1.83787706640935 = log(2pi)</span>
+<a name="l01158"></a>01158                 <span class="keywordtype">double</span> tmp=-0.5* ( <a class="code" href="classbdm_1_1enorm.html#2d92dde696b2a7a5b10ddef5d22ba2c2" title="Covariance matrix in decomposed form.">R</a>.invqform ( <a class="code" href="classbdm_1_1enorm.html#c702a194720853570d08b65482f842c7" title="mean value">mu</a>-val ) );<span class="comment">// - lognc();</span>
+<a name="l01159"></a>01159                 <span class="keywordflow">return</span>  tmp;
+<a name="l01160"></a>01160         };
+<a name="l01161"></a>01161 
+<a name="l01162"></a>01162         <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt;
+<a name="l01163"></a><a class="code" href="classbdm_1_1enorm.html#25785343aff102cc5df1cab08ba16d32">01163</a>         <span class="keyword">inline</span> <span class="keywordtype">double</span> <a class="code" href="classbdm_1_1enorm.html#25785343aff102cc5df1cab08ba16d32" title="logarithm of the normalizing constant, ">enorm&lt;sq_T&gt;::lognc</a> ()<span class="keyword"> const</span>
+<a name="l01164"></a>01164 <span class="keyword">        </span>{
+<a name="l01165"></a>01165                 <span class="comment">// 1.83787706640935 = log(2pi)</span>
+<a name="l01166"></a>01166                 <span class="keywordtype">double</span> tmp=0.5* ( <a class="code" href="classbdm_1_1enorm.html#2d92dde696b2a7a5b10ddef5d22ba2c2" title="Covariance matrix in decomposed form.">R</a>.cols() * 1.83787706640935 +<a class="code" href="classbdm_1_1enorm.html#2d92dde696b2a7a5b10ddef5d22ba2c2" title="Covariance matrix in decomposed form.">R</a>.logdet() );
+<a name="l01167"></a>01167                 <span class="keywordflow">return</span> tmp;
+<a name="l01168"></a>01168         };
+<a name="l01169"></a>01169 
+<a name="l01170"></a>01170         <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt;
+<a name="l01171"></a><a class="code" href="classbdm_1_1mlnorm.html#5d18dec3167584338a4775c1d165d96f">01171</a>         <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1mlnorm.html#5d18dec3167584338a4775c1d165d96f" title="Set A and R.">mlnorm&lt;sq_T&gt;::set_parameters</a> ( <span class="keyword">const</span> mat &amp;A0, <span class="keyword">const</span> vec &amp;mu0, <span class="keyword">const</span> sq_T &amp;R0 )
+<a name="l01172"></a>01172         {
+<a name="l01173"></a>01173                 it_assert_debug ( A0.rows() ==mu0.length(),<span class="stringliteral">""</span> );
+<a name="l01174"></a>01174                 it_assert_debug ( A0.rows() ==R0.rows(),<span class="stringliteral">""</span> );
+<a name="l01175"></a>01175 
+<a name="l01176"></a>01176                 <a class="code" href="classbdm_1_1epdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a>.set_parameters ( zeros ( A0.rows() ),R0 );
+<a name="l01177"></a>01177                 A = A0;
+<a name="l01178"></a>01178                 mu_const = mu0;
+<a name="l01179"></a>01179                 <a class="code" href="classbdm_1_1mpdf.html#7c1900976ff13dbc09c9729b3bbff9e6" title="dimension of the condition">dimc</a>=A0.cols();
+<a name="l01180"></a>01180         }
+<a name="l01181"></a>01181 
+<a name="l01182"></a>01182 <span class="comment">// template&lt;class sq_T&gt;</span>
+<a name="l01183"></a>01183 <span class="comment">// vec mlnorm&lt;sq_T&gt;::samplecond (const  vec &amp;cond, double &amp;lik ) {</span>
+<a name="l01184"></a>01184 <span class="comment">//      this-&gt;condition ( cond );</span>
+<a name="l01185"></a>01185 <span class="comment">//      vec smp = epdf.sample();</span>
+<a name="l01186"></a>01186 <span class="comment">//      lik = epdf.eval ( smp );</span>
+<a name="l01187"></a>01187 <span class="comment">//      return smp;</span>
+<a name="l01188"></a>01188 <span class="comment">// }</span>
+<a name="l01189"></a>01189 
+<a name="l01190"></a>01190 <span class="comment">// template&lt;class sq_T&gt;</span>
+<a name="l01191"></a>01191 <span class="comment">// mat mlnorm&lt;sq_T&gt;::samplecond (const vec &amp;cond, vec &amp;lik, int n ) {</span>
+<a name="l01192"></a>01192 <span class="comment">//      int i;</span>
+<a name="l01193"></a>01193 <span class="comment">//      int dim = rv.count();</span>
+<a name="l01194"></a>01194 <span class="comment">//      mat Smp ( dim,n );</span>
+<a name="l01195"></a>01195 <span class="comment">//      vec smp ( dim );</span>
+<a name="l01196"></a>01196 <span class="comment">//      this-&gt;condition ( cond );</span>
+<a name="l01197"></a>01197 <span class="comment">//</span>
+<a name="l01198"></a>01198 <span class="comment">//      for ( i=0; i&lt;n; i++ ) {</span>
+<a name="l01199"></a>01199 <span class="comment">//              smp = epdf.sample();</span>
+<a name="l01200"></a>01200 <span class="comment">//              lik ( i ) = epdf.eval ( smp );</span>
+<a name="l01201"></a>01201 <span class="comment">//              Smp.set_col ( i ,smp );</span>
+<a name="l01202"></a>01202 <span class="comment">//      }</span>
+<a name="l01203"></a>01203 <span class="comment">//</span>
+<a name="l01204"></a>01204 <span class="comment">//      return Smp;</span>
+<a name="l01205"></a>01205 <span class="comment">// }</span>
 <a name="l01206"></a>01206 
-<a name="l01207"></a>01207                 <a class="code" href="classbdm_1_1enorm.html" title="Gaussian density with positive definite (decomposed) covariance matrix.">enorm&lt;sq_T&gt;</a>* tmp = <span class="keyword">new</span> <a class="code" href="classbdm_1_1enorm.html" title="Gaussian density with positive definite (decomposed) covariance matrix.">enorm&lt;sq_T&gt;</a>;
-<a name="l01208"></a>01208                 tmp-&gt;<a class="code" href="classbdm_1_1epdf.html#f423e28448dbb69ef4905295ec8de8ff" title="Name its rv.">set_rv</a> ( rvn );
-<a name="l01209"></a>01209                 tmp-&gt;<a class="code" href="classbdm_1_1enorm.html#b8322f2c11560871dd922c660f4771bb">set_parameters</a> ( <a class="code" href="classbdm_1_1enorm.html#c702a194720853570d08b65482f842c7" title="mean value">mu</a> ( irvn ), Rn );
-<a name="l01210"></a>01210                 <span class="keywordflow">return</span> tmp;
-<a name="l01211"></a>01211         }
-<a name="l01212"></a>01212 
-<a name="l01213"></a>01213         <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt;
-<a name="l01214"></a><a class="code" href="classbdm_1_1enorm.html#baea4d49c657342b58297d68cda16d26">01214</a>         <a class="code" href="classbdm_1_1mpdf.html" title="Conditional probability density, e.g. modeling some dependencies.">mpdf</a>* <a class="code" href="classbdm_1_1enorm.html#baea4d49c657342b58297d68cda16d26" title="Return conditional density on the given RV, the remaining rvs will be in conditioning...">enorm&lt;sq_T&gt;::condition</a> ( <span class="keyword">const</span> <a class="code" href="classbdm_1_1RV.html" title="Class representing variables, most often random variables.">RV</a> &amp;rvn )<span class="keyword"> const</span>
-<a name="l01215"></a>01215 <span class="keyword">        </span>{
-<a name="l01216"></a>01216 
-<a name="l01217"></a>01217                 it_assert_debug ( <a class="code" href="classbdm_1_1epdf.html#c4b863ff84c7a4882fb3ad18556027f9" title="True if rv is assigned.">isnamed</a>(),<span class="stringliteral">"rvs are not assigned"</span> );
-<a name="l01218"></a>01218 
-<a name="l01219"></a>01219                 <a class="code" href="classbdm_1_1RV.html" title="Class representing variables, most often random variables.">RV</a> rvc = <a class="code" href="classbdm_1_1epdf.html#62c5b8ff71d9ebe6cd58d3c342eb1dc8" title="Description of the random variable.">rv</a>.<a class="code" href="classbdm_1_1RV.html#aec44dabdf0a6d90fbae95e1356eda39" title="Subtract another variable from the current one.">subt</a> ( rvn );
-<a name="l01220"></a>01220                 it_assert_debug ( ( rvc.<a class="code" href="classbdm_1_1RV.html#de30156104f61d86c94f758861418089">_dsize</a>() +rvn.<a class="code" href="classbdm_1_1RV.html#de30156104f61d86c94f758861418089">_dsize</a>() ==<a class="code" href="classbdm_1_1epdf.html#62c5b8ff71d9ebe6cd58d3c342eb1dc8" title="Description of the random variable.">rv</a>.<a class="code" href="classbdm_1_1RV.html#de30156104f61d86c94f758861418089">_dsize</a>() ),<span class="stringliteral">"wrong rvn"</span> );
-<a name="l01221"></a>01221                 <span class="comment">//Permutation vector of the new R</span>
-<a name="l01222"></a>01222                 ivec irvn = rvn.<a class="code" href="classbdm_1_1RV.html#cbebdb5e0d30101a6eb63550ef701c55">dataind</a> ( <a class="code" href="classbdm_1_1epdf.html#62c5b8ff71d9ebe6cd58d3c342eb1dc8" title="Description of the random variable.">rv</a> );
-<a name="l01223"></a>01223                 ivec irvc = rvc.<a class="code" href="classbdm_1_1RV.html#cbebdb5e0d30101a6eb63550ef701c55">dataind</a> ( <a class="code" href="classbdm_1_1epdf.html#62c5b8ff71d9ebe6cd58d3c342eb1dc8" title="Description of the random variable.">rv</a> );
-<a name="l01224"></a>01224                 ivec perm=concat ( irvn , irvc );
-<a name="l01225"></a>01225                 sq_T Rn ( <a class="code" href="classbdm_1_1enorm.html#2d92dde696b2a7a5b10ddef5d22ba2c2" title="Covariance matrix in decomposed form.">R</a>,perm );
-<a name="l01226"></a>01226 
-<a name="l01227"></a>01227                 <span class="comment">//fixme - could this be done in general for all sq_T?</span>
-<a name="l01228"></a>01228                 mat S=Rn.to_mat();
-<a name="l01229"></a>01229                 <span class="comment">//fixme</span>
-<a name="l01230"></a>01230                 <span class="keywordtype">int</span> n=rvn.<a class="code" href="classbdm_1_1RV.html#de30156104f61d86c94f758861418089">_dsize</a>()-1;
-<a name="l01231"></a>01231                 <span class="keywordtype">int</span> end=<a class="code" href="classbdm_1_1enorm.html#2d92dde696b2a7a5b10ddef5d22ba2c2" title="Covariance matrix in decomposed form.">R</a>.rows()-1;
-<a name="l01232"></a>01232                 mat S11 = S.get ( 0,n, 0, n );
-<a name="l01233"></a>01233                 mat S12 = S.get ( 0, n , rvn.<a class="code" href="classbdm_1_1RV.html#de30156104f61d86c94f758861418089">_dsize</a>(), end );
-<a name="l01234"></a>01234                 mat S22 = S.get ( rvn.<a class="code" href="classbdm_1_1RV.html#de30156104f61d86c94f758861418089">_dsize</a>(), end, rvn.<a class="code" href="classbdm_1_1RV.html#de30156104f61d86c94f758861418089">_dsize</a>(), end );
-<a name="l01235"></a>01235 
-<a name="l01236"></a>01236                 vec mu1 = <a class="code" href="classbdm_1_1enorm.html#c702a194720853570d08b65482f842c7" title="mean value">mu</a> ( irvn );
-<a name="l01237"></a>01237                 vec mu2 = <a class="code" href="classbdm_1_1enorm.html#c702a194720853570d08b65482f842c7" title="mean value">mu</a> ( irvc );
-<a name="l01238"></a>01238                 mat A=S12*inv ( S22 );
-<a name="l01239"></a>01239                 sq_T R_n ( S11 - A *S12.T() );
-<a name="l01240"></a>01240 
-<a name="l01241"></a>01241                 <a class="code" href="classbdm_1_1mlnorm.html" title="Normal distributed linear function with linear function of mean value;.">mlnorm&lt;sq_T&gt;</a>* tmp=<span class="keyword">new</span> <a class="code" href="classbdm_1_1mlnorm.html" title="Normal distributed linear function with linear function of mean value;.">mlnorm&lt;sq_T&gt;</a> ( );
-<a name="l01242"></a>01242                 tmp-&gt;set_rv ( rvn ); tmp-&gt;set_rvc ( rvc );
-<a name="l01243"></a>01243                 tmp-&gt;set_parameters ( A,mu1-A*mu2,R_n );
-<a name="l01244"></a>01244                 <span class="keywordflow">return</span> tmp;
-<a name="l01245"></a>01245         }
-<a name="l01246"></a>01246 
-<a name="l01248"></a>01248 
-<a name="l01249"></a>01249         <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt;
-<a name="l01250"></a>01250         std::ostream &amp;operator&lt;&lt; ( std::ostream &amp;os,  mlnorm&lt;sq_T&gt; &amp;ml )
-<a name="l01251"></a>01251         {
-<a name="l01252"></a>01252                 os &lt;&lt; <span class="stringliteral">"A:"</span>&lt;&lt; ml.A&lt;&lt;endl;
-<a name="l01253"></a>01253                 os &lt;&lt; <span class="stringliteral">"mu:"</span>&lt;&lt; ml.mu_const&lt;&lt;endl;
-<a name="l01254"></a>01254                 os &lt;&lt; <span class="stringliteral">"R:"</span> &lt;&lt; ml.epdf._R().to_mat() &lt;&lt;endl;
-<a name="l01255"></a>01255                 <span class="keywordflow">return</span> os;
-<a name="l01256"></a>01256         };
-<a name="l01257"></a>01257 
-<a name="l01258"></a>01258 }
-<a name="l01259"></a>01259 <span class="preprocessor">#endif //EF_H</span>
+<a name="l01207"></a>01207         <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt;
+<a name="l01208"></a><a class="code" href="classbdm_1_1mlnorm.html#0dafc0196e7e07fd06dc6716e0e18bc2">01208</a>         <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1mlnorm.html#0dafc0196e7e07fd06dc6716e0e18bc2">mlnorm&lt;sq_T&gt;::condition</a> ( <span class="keyword">const</span> vec &amp;cond )
+<a name="l01209"></a>01209         {
+<a name="l01210"></a>01210                 _mu = A*cond + mu_const;
+<a name="l01211"></a>01211 <span class="comment">//R is already assigned;</span>
+<a name="l01212"></a>01212         }
+<a name="l01213"></a>01213 
+<a name="l01214"></a>01214         <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt;
+<a name="l01215"></a><a class="code" href="classbdm_1_1enorm.html#c2996bdaffad38fbe0fc776db54c9d80">01215</a>         <a class="code" href="classbdm_1_1enorm.html" title="Gaussian density with positive definite (decomposed) covariance matrix.">enorm&lt;sq_T&gt;</a>* <a class="code" href="classbdm_1_1enorm.html#c2996bdaffad38fbe0fc776db54c9d80" title="Return marginal density on the given RV, the remainig rvs are intergrated out.">enorm&lt;sq_T&gt;::marginal</a> ( <span class="keyword">const</span> <a class="code" href="classbdm_1_1RV.html" title="Class representing variables, most often random variables.">RV</a> &amp;rvn )<span class="keyword"> const</span>
+<a name="l01216"></a>01216 <span class="keyword">        </span>{
+<a name="l01217"></a>01217                 it_assert_debug ( <a class="code" href="classbdm_1_1epdf.html#c4b863ff84c7a4882fb3ad18556027f9" title="True if rv is assigned.">isnamed</a>(), <span class="stringliteral">"rv description is not assigned"</span> );
+<a name="l01218"></a>01218                 ivec irvn = rvn.<a class="code" href="classbdm_1_1RV.html#cbebdb5e0d30101a6eb63550ef701c55">dataind</a> ( <a class="code" href="classbdm_1_1epdf.html#62c5b8ff71d9ebe6cd58d3c342eb1dc8" title="Description of the random variable.">rv</a> );
+<a name="l01219"></a>01219 
+<a name="l01220"></a>01220                 sq_T Rn ( <a class="code" href="classbdm_1_1enorm.html#2d92dde696b2a7a5b10ddef5d22ba2c2" title="Covariance matrix in decomposed form.">R</a>,irvn ); <span class="comment">//select rows and columns of R</span>
+<a name="l01221"></a>01221 
+<a name="l01222"></a>01222                 <a class="code" href="classbdm_1_1enorm.html" title="Gaussian density with positive definite (decomposed) covariance matrix.">enorm&lt;sq_T&gt;</a>* tmp = <span class="keyword">new</span> <a class="code" href="classbdm_1_1enorm.html" title="Gaussian density with positive definite (decomposed) covariance matrix.">enorm&lt;sq_T&gt;</a>;
+<a name="l01223"></a>01223                 tmp-&gt;<a class="code" href="classbdm_1_1epdf.html#f423e28448dbb69ef4905295ec8de8ff" title="Name its rv.">set_rv</a> ( rvn );
+<a name="l01224"></a>01224                 tmp-&gt;<a class="code" href="classbdm_1_1enorm.html#b8322f2c11560871dd922c660f4771bb">set_parameters</a> ( <a class="code" href="classbdm_1_1enorm.html#c702a194720853570d08b65482f842c7" title="mean value">mu</a> ( irvn ), Rn );
+<a name="l01225"></a>01225                 <span class="keywordflow">return</span> tmp;
+<a name="l01226"></a>01226         }
+<a name="l01227"></a>01227 
+<a name="l01228"></a>01228         <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt;
+<a name="l01229"></a><a class="code" href="classbdm_1_1enorm.html#baea4d49c657342b58297d68cda16d26">01229</a>         <a class="code" href="classbdm_1_1mpdf.html" title="Conditional probability density, e.g. modeling some dependencies.">mpdf</a>* <a class="code" href="classbdm_1_1enorm.html#baea4d49c657342b58297d68cda16d26" title="Return conditional density on the given RV, the remaining rvs will be in conditioning...">enorm&lt;sq_T&gt;::condition</a> ( <span class="keyword">const</span> <a class="code" href="classbdm_1_1RV.html" title="Class representing variables, most often random variables.">RV</a> &amp;rvn )<span class="keyword"> const</span>
+<a name="l01230"></a>01230 <span class="keyword">        </span>{
+<a name="l01231"></a>01231 
+<a name="l01232"></a>01232                 it_assert_debug ( <a class="code" href="classbdm_1_1epdf.html#c4b863ff84c7a4882fb3ad18556027f9" title="True if rv is assigned.">isnamed</a>(),<span class="stringliteral">"rvs are not assigned"</span> );
+<a name="l01233"></a>01233 
+<a name="l01234"></a>01234                 <a class="code" href="classbdm_1_1RV.html" title="Class representing variables, most often random variables.">RV</a> rvc = <a class="code" href="classbdm_1_1epdf.html#62c5b8ff71d9ebe6cd58d3c342eb1dc8" title="Description of the random variable.">rv</a>.<a class="code" href="classbdm_1_1RV.html#aec44dabdf0a6d90fbae95e1356eda39" title="Subtract another variable from the current one.">subt</a> ( rvn );
+<a name="l01235"></a>01235                 it_assert_debug ( ( rvc.<a class="code" href="classbdm_1_1RV.html#de30156104f61d86c94f758861418089">_dsize</a>() +rvn.<a class="code" href="classbdm_1_1RV.html#de30156104f61d86c94f758861418089">_dsize</a>() ==<a class="code" href="classbdm_1_1epdf.html#62c5b8ff71d9ebe6cd58d3c342eb1dc8" title="Description of the random variable.">rv</a>.<a class="code" href="classbdm_1_1RV.html#de30156104f61d86c94f758861418089">_dsize</a>() ),<span class="stringliteral">"wrong rvn"</span> );
+<a name="l01236"></a>01236                 <span class="comment">//Permutation vector of the new R</span>
+<a name="l01237"></a>01237                 ivec irvn = rvn.<a class="code" href="classbdm_1_1RV.html#cbebdb5e0d30101a6eb63550ef701c55">dataind</a> ( <a class="code" href="classbdm_1_1epdf.html#62c5b8ff71d9ebe6cd58d3c342eb1dc8" title="Description of the random variable.">rv</a> );
+<a name="l01238"></a>01238                 ivec irvc = rvc.<a class="code" href="classbdm_1_1RV.html#cbebdb5e0d30101a6eb63550ef701c55">dataind</a> ( <a class="code" href="classbdm_1_1epdf.html#62c5b8ff71d9ebe6cd58d3c342eb1dc8" title="Description of the random variable.">rv</a> );
+<a name="l01239"></a>01239                 ivec perm=concat ( irvn , irvc );
+<a name="l01240"></a>01240                 sq_T Rn ( <a class="code" href="classbdm_1_1enorm.html#2d92dde696b2a7a5b10ddef5d22ba2c2" title="Covariance matrix in decomposed form.">R</a>,perm );
+<a name="l01241"></a>01241 
+<a name="l01242"></a>01242                 <span class="comment">//fixme - could this be done in general for all sq_T?</span>
+<a name="l01243"></a>01243                 mat S=Rn.to_mat();
+<a name="l01244"></a>01244                 <span class="comment">//fixme</span>
+<a name="l01245"></a>01245                 <span class="keywordtype">int</span> n=rvn.<a class="code" href="classbdm_1_1RV.html#de30156104f61d86c94f758861418089">_dsize</a>()-1;
+<a name="l01246"></a>01246                 <span class="keywordtype">int</span> end=<a class="code" href="classbdm_1_1enorm.html#2d92dde696b2a7a5b10ddef5d22ba2c2" title="Covariance matrix in decomposed form.">R</a>.rows()-1;
+<a name="l01247"></a>01247                 mat S11 = S.get ( 0,n, 0, n );
+<a name="l01248"></a>01248                 mat S12 = S.get ( 0, n , rvn.<a class="code" href="classbdm_1_1RV.html#de30156104f61d86c94f758861418089">_dsize</a>(), end );
+<a name="l01249"></a>01249                 mat S22 = S.get ( rvn.<a class="code" href="classbdm_1_1RV.html#de30156104f61d86c94f758861418089">_dsize</a>(), end, rvn.<a class="code" href="classbdm_1_1RV.html#de30156104f61d86c94f758861418089">_dsize</a>(), end );
+<a name="l01250"></a>01250 
+<a name="l01251"></a>01251                 vec mu1 = <a class="code" href="classbdm_1_1enorm.html#c702a194720853570d08b65482f842c7" title="mean value">mu</a> ( irvn );
+<a name="l01252"></a>01252                 vec mu2 = <a class="code" href="classbdm_1_1enorm.html#c702a194720853570d08b65482f842c7" title="mean value">mu</a> ( irvc );
+<a name="l01253"></a>01253                 mat A=S12*inv ( S22 );
+<a name="l01254"></a>01254                 sq_T R_n ( S11 - A *S12.T() );
+<a name="l01255"></a>01255 
+<a name="l01256"></a>01256                 <a class="code" href="classbdm_1_1mlnorm.html" title="Normal distributed linear function with linear function of mean value;.">mlnorm&lt;sq_T&gt;</a>* tmp=<span class="keyword">new</span> <a class="code" href="classbdm_1_1mlnorm.html" title="Normal distributed linear function with linear function of mean value;.">mlnorm&lt;sq_T&gt;</a> ( );
+<a name="l01257"></a>01257                 tmp-&gt;set_rv ( rvn ); tmp-&gt;set_rvc ( rvc );
+<a name="l01258"></a>01258                 tmp-&gt;set_parameters ( A,mu1-A*mu2,R_n );
+<a name="l01259"></a>01259                 <span class="keywordflow">return</span> tmp;
+<a name="l01260"></a>01260         }
+<a name="l01261"></a>01261 
+<a name="l01263"></a>01263 
+<a name="l01264"></a>01264         <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt;
+<a name="l01265"></a>01265         std::ostream &amp;operator&lt;&lt; ( std::ostream &amp;os,  mlnorm&lt;sq_T&gt; &amp;ml )
+<a name="l01266"></a>01266         {
+<a name="l01267"></a>01267                 os &lt;&lt; <span class="stringliteral">"A:"</span>&lt;&lt; ml.A&lt;&lt;endl;
+<a name="l01268"></a>01268                 os &lt;&lt; <span class="stringliteral">"mu:"</span>&lt;&lt; ml.mu_const&lt;&lt;endl;
+<a name="l01269"></a>01269                 os &lt;&lt; <span class="stringliteral">"R:"</span> &lt;&lt; ml.epdf._R().to_mat() &lt;&lt;endl;
+<a name="l01270"></a>01270                 <span class="keywordflow">return</span> os;
+<a name="l01271"></a>01271         };
+<a name="l01272"></a>01272 
+<a name="l01273"></a>01273 }
+<a name="l01274"></a>01274 <span class="preprocessor">#endif //EF_H</span>
 </pre></div></div>
-<hr size="1"><address style="text-align: right;"><small>Generated on Mon Jun 15 13:47:07 2009 for mixpp by&nbsp;
+<hr size="1"><address style="text-align: right;"><small>Generated on Wed Jun 17 14:13:28 2009 for mixpp by&nbsp;
 <a href="http://www.doxygen.org/index.html">
 <img src="doxygen.png" alt="doxygen" align="middle" border="0"></a> 1.5.8 </small></address>
