Index: doc/html/libEF_8h-source.html
===================================================================
--- doc/html/libEF_8h-source.html (revision 270)
+++ doc/html/libEF_8h-source.html (revision 271)
@@ -6,11 +6,51 @@
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@@ -29,7 +69,7 @@
 <a name="l00022"></a>00022 
 <a name="l00023"></a>00023 
-<a name="l00025"></a>00025 <span class="keyword">extern</span> Uniform_RNG <a class="code" href="namespacebdm.html#96288dbda6916cd442af735f66a9f40b" title="Global Uniform_RNG.">UniRNG</a>;
-<a name="l00027"></a>00027 <span class="keyword">extern</span> Normal_RNG <a class="code" href="namespacebdm.html#c959a7382efbcc31af4b58cf0f0f951a" title="Global Normal_RNG.">NorRNG</a>;
-<a name="l00029"></a>00029 <span class="keyword">extern</span> Gamma_RNG <a class="code" href="namespacebdm.html#2828dc833cc283a1fb39a5e8dc06518f" title="Global Gamma_RNG.">GamRNG</a>;
+<a name="l00025"></a>00025 <span class="keyword">extern</span> Uniform_RNG UniRNG;
+<a name="l00027"></a>00027 <span class="keyword">extern</span> Normal_RNG NorRNG;
+<a name="l00029"></a>00029 <span class="keyword">extern</span> Gamma_RNG GamRNG;
 <a name="l00030"></a>00030 
 <a name="l00037"></a><a class="code" href="classbdm_1_1eEF.html">00037</a> <span class="keyword">class </span><a class="code" href="classbdm_1_1eEF.html" title="General conjugate exponential family posterior density.">eEF</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> {
@@ -81,10 +121,10 @@
 <a name="l00113"></a><a class="code" href="classbdm_1_1enorm.html#2d92dde696b2a7a5b10ddef5d22ba2c2">00113</a>         sq_T <a class="code" href="classbdm_1_1enorm.html#2d92dde696b2a7a5b10ddef5d22ba2c2" title="Covariance matrix in decomposed form.">R</a>;
 <a name="l00114"></a>00114 <span class="keyword">public</span>:
-<a name="l00117"></a>00117         
-<a name="l00118"></a>00118         <a class="code" href="classbdm_1_1enorm.html" title="Gaussian density with positive definite (decomposed) covariance matrix.">enorm</a> ( );
-<a name="l00119"></a>00119         <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="l00117"></a>00117 
+<a name="l00118"></a>00118         <a class="code" href="classbdm_1_1enorm.html" title="Gaussian density with positive definite (decomposed) covariance matrix.">enorm</a> ( ):<a class="code" href="classbdm_1_1eEF.html" title="General conjugate exponential family posterior density.">eEF</a> ( ), <a class="code" href="classbdm_1_1enorm.html#c702a194720853570d08b65482f842c7" title="mean value">mu</a> ( ),<a class="code" href="classbdm_1_1enorm.html#2d92dde696b2a7a5b10ddef5d22ba2c2" title="Covariance matrix in decomposed form.">R</a> ( ) {};
+<a name="l00119"></a>00119         <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="l00120"></a>00120         <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="l00122"></a>00122                 
-<a name="l00125"></a>00125         
+<a name="l00122"></a>00122 
+<a name="l00125"></a>00125 
 <a name="l00127"></a>00127         <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="l00128"></a>00128 
@@ -99,11 +139,11 @@
 <a name="l00137"></a>00137 <span class="comment">//      enorm&lt;sq_T&gt;* marginal ( const RV &amp;rv ) const;</span>
 <a name="l00138"></a>00138         <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_1enorm.html#cd02d76e9d4f96bdd3fa6b604e273039" 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="l00140"></a>00140         
-<a name="l00143"></a>00143         
+<a name="l00140"></a>00140 
+<a name="l00143"></a>00143 
 <a name="l00144"></a>00144         vec&amp; _mu() {<span class="keywordflow">return</span> <a class="code" href="classbdm_1_1enorm.html#c702a194720853570d08b65482f842c7" title="mean value">mu</a>;}
 <a name="l00145"></a>00145         <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="l00146"></a>00146         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="l00147"></a>00147         <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="l00149"></a>00149         
+<a name="l00149"></a>00149 
 <a name="l00150"></a>00150 };
 <a name="l00151"></a>00151 
@@ -116,14 +156,14 @@
 <a name="l00168"></a>00168 <span class="keyword">public</span>:
 <a name="l00171"></a>00171         <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="l00172"></a>00172         <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="l00173"></a>00173         
-<a name="l00174"></a>00174         <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="l00172"></a>00172         <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="l00173"></a>00173 
+<a name="l00174"></a>00174         <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="l00175"></a>00175                 <a class="code" href="classbdm_1_1egiw.html#23e4d78bea7e98840f3da30e76a2b57a" title="Dimension of the output.">dimx</a>=dimx0;
-<a name="l00176"></a>00176                 <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="l00177"></a>00177                 <a class="code" href="classbdm_1_1egiw.html#322414c32d9a21a006a5aab0311f64fd" title="Dimension of the regressor.">nPsi</a> = <a class="code" href="classbdm_1_1egiw.html#ae56852845c6af176fd9017dbebbbd52" title="Extended information matrix of sufficient statistics.">V</a>.<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="l00178"></a>00178                 
+<a name="l00176"></a>00176                 <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="l00177"></a>00177                 <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="l00178"></a>00178 
 <a name="l00179"></a>00179                 <a class="code" href="classbdm_1_1egiw.html#ae56852845c6af176fd9017dbebbbd52" title="Extended information matrix of sufficient statistics.">V</a>=V0;
 <a name="l00180"></a>00180                 <span class="keywordflow">if</span> ( nu0&lt;0 ) {
-<a name="l00181"></a>00181                         <a class="code" href="classbdm_1_1egiw.html#447eacf19d4f4083872686f044814dc4" title="Number of data records (degrees of freedom) of sufficient statistics.">nu</a> = 0.1 +nPsi +2*xdim +2; <span class="comment">// +2 assures finite expected value of R</span>
+<a name="l00181"></a>00181                         <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="l00182"></a>00182                         <span class="comment">// terms before that are sufficient for finite normalization</span>
 <a name="l00183"></a>00183                 }
@@ -139,9 +179,9 @@
 <a name="l00195"></a>00195         <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="l00196"></a>00196         <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="l00197"></a><a class="code" href="classbdm_1_1egiw.html#8e610e95401a11baf34f65e16ecd87be">00197</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 ) {<a class="code" href="classbdm_1_1egiw.html#ae56852845c6af176fd9017dbebbbd52" title="Extended information matrix of sufficient statistics.">V</a>*=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="l00197"></a><a class="code" href="classbdm_1_1egiw.html#8e610e95401a11baf34f65e16ecd87be">00197</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="l00198"></a>00198 
-<a name="l00201"></a>00201         
-<a name="l00202"></a>00202         <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> <a class="code" href="classbdm_1_1egiw.html#ae56852845c6af176fd9017dbebbbd52" title="Extended information matrix of sufficient statistics.">V</a>;}
-<a name="l00203"></a>00203         <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> <a class="code" href="classbdm_1_1egiw.html#ae56852845c6af176fd9017dbebbbd52" title="Extended information matrix of sufficient statistics.">V</a>;}
+<a name="l00201"></a>00201 
+<a name="l00202"></a>00202         <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="l00203"></a>00203         <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="l00204"></a>00204         <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="l00205"></a>00205         <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>;}
@@ -153,480 +193,488 @@
 <a name="l00222"></a><a class="code" href="classbdm_1_1eDirich.html#ee9db192a6f0ab7b29c33b2a18a5e1b4">00222</a>         <span class="keywordtype">double</span> <a class="code" href="classbdm_1_1eDirich.html#ee9db192a6f0ab7b29c33b2a18a5e1b4" title="speedup variable">gamma</a>;
 <a name="l00223"></a>00223 <span class="keyword">public</span>:
-<a name="l00225"></a><a class="code" href="classbdm_1_1eDirich.html#d5137485050ca8d67549b514896f602d">00225</a>         <a class="code" href="classbdm_1_1eDirich.html#d5137485050ca8d67549b514896f602d" title="Default constructor.">eDirich</a> () : <a class="code" href="classbdm_1_1eEF.html" title="General conjugate exponential family posterior density.">eEF</a> ( ) {};
-<a name="l00227"></a><a class="code" href="classbdm_1_1eDirich.html#31cc8bf709552c9e7286ac16b27c8e2c">00227</a>         <a class="code" href="classbdm_1_1eDirich.html#d5137485050ca8d67549b514896f602d" title="Default constructor.">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> () {<a class="code" href="classbdm_1_1eDirich.html#a06af2376976a33e1eceaed7e8da75a5" title="Set internal parameters.">set_parameters</a> ( D0.<a class="code" href="classbdm_1_1eDirich.html#f25886a49b4667af61245de81c83b5d2" title="sufficient statistics">beta</a> );};
-<a name="l00228"></a><a class="code" href="classbdm_1_1eDirich.html#3290613d31d58daa8a45a54b003871fc">00228</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="l00229"></a><a class="code" href="classbdm_1_1eDirich.html#cb343355ec791298bb5a3404cd482fb6">00229</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>/<a class="code" href="classbdm_1_1eDirich.html#ee9db192a6f0ab7b29c33b2a18a5e1b4" title="speedup variable">gamma</a>;};
-<a name="l00230"></a><a class="code" href="classbdm_1_1eDirich.html#43c547a2507e233706f92712d8c2aacc">00230</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="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 ) ) / ( <a class="code" href="classbdm_1_1eDirich.html#ee9db192a6f0ab7b29c33b2a18a5e1b4" title="speedup variable">gamma</a>* ( <a class="code" href="classbdm_1_1eDirich.html#ee9db192a6f0ab7b29c33b2a18a5e1b4" title="speedup variable">gamma</a>+1 ) );}
-<a name="l00232"></a><a class="code" href="classbdm_1_1eDirich.html#e09a24938e80c3d94b0ee842d1552318">00232</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="l00233"></a>00233                 <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="l00234"></a>00234                 <span class="keywordflow">return</span> tmp;
-<a name="l00235"></a>00235         };
-<a name="l00236"></a><a class="code" href="classbdm_1_1eDirich.html#279a99f6266c82fe2273e83841f19eb2">00236</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="l00237"></a>00237                 <span class="keywordtype">double</span> tmp;
-<a name="l00238"></a>00238                 <span class="keywordtype">double</span> gam=sum ( <a class="code" href="classbdm_1_1eDirich.html#f25886a49b4667af61245de81c83b5d2" title="sufficient statistics">beta</a> );
-<a name="l00239"></a>00239                 <span class="keywordtype">double</span> lgb=0.0;
-<a name="l00240"></a>00240                 <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="l00241"></a>00241                 tmp= lgb-lgamma ( gam );
-<a name="l00242"></a>00242                 it_assert_debug ( std::isfinite ( tmp ),<span class="stringliteral">"Infinite value"</span> );
+<a name="l00226"></a>00226 
+<a name="l00227"></a>00227         <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="l00228"></a>00228         <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="l00229"></a>00229         eDirich ( <span class="keyword">const</span> vec &amp;beta0 ) {set_parameters ( beta0 );};
+<a name="l00230"></a>00230         <span class="keywordtype">void</span> set_parameters ( <span class="keyword">const</span> vec &amp;beta0 ) {
+<a name="l00231"></a>00231                 <a class="code" href="classbdm_1_1eDirich.html#f25886a49b4667af61245de81c83b5d2" title="sufficient statistics">beta</a>= beta0;
+<a name="l00232"></a>00232                 <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="l00233"></a>00233                 <a class="code" href="classbdm_1_1eDirich.html#ee9db192a6f0ab7b29c33b2a18a5e1b4" title="speedup variable">gamma</a> = sum ( <a class="code" href="classbdm_1_1eDirich.html#f25886a49b4667af61245de81c83b5d2" title="sufficient statistics">beta</a> );
+<a name="l00234"></a>00234         }
+<a name="l00236"></a>00236 
+<a name="l00237"></a><a class="code" href="classbdm_1_1eDirich.html#3290613d31d58daa8a45a54b003871fc">00237</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="l00238"></a><a class="code" href="classbdm_1_1eDirich.html#cb343355ec791298bb5a3404cd482fb6">00238</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>/<a class="code" href="classbdm_1_1eDirich.html#ee9db192a6f0ab7b29c33b2a18a5e1b4" title="speedup variable">gamma</a>;};
+<a name="l00239"></a><a class="code" href="classbdm_1_1eDirich.html#43c547a2507e233706f92712d8c2aacc">00239</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="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 ) ) / ( <a class="code" href="classbdm_1_1eDirich.html#ee9db192a6f0ab7b29c33b2a18a5e1b4" title="speedup variable">gamma</a>* ( <a class="code" href="classbdm_1_1eDirich.html#ee9db192a6f0ab7b29c33b2a18a5e1b4" title="speedup variable">gamma</a>+1 ) );}
+<a name="l00241"></a><a class="code" href="classbdm_1_1eDirich.html#e09a24938e80c3d94b0ee842d1552318">00241</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="l00242"></a>00242                 <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="l00243"></a>00243                 <span class="keywordflow">return</span> tmp;
 <a name="l00244"></a>00244         };
-<a name="l00246"></a><a class="code" href="classbdm_1_1eDirich.html#175e0add26d2105c28d8121eefb9e324">00246</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="l00248"></a><a class="code" href="classbdm_1_1eDirich.html#a06af2376976a33e1eceaed7e8da75a5">00248</a>         <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1eDirich.html#a06af2376976a33e1eceaed7e8da75a5" title="Set internal parameters.">set_parameters</a> ( <span class="keyword">const</span> vec &amp;beta0 ) {
-<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 class="code" href="classbdm_1_1eDirich.html#ee9db192a6f0ab7b29c33b2a18a5e1b4" title="speedup variable">gamma</a> = sum ( <a class="code" href="classbdm_1_1eDirich.html#f25886a49b4667af61245de81c83b5d2" title="sufficient statistics">beta</a> );
-<a name="l00252"></a>00252         }
-<a name="l00253"></a>00253 };
-<a name="l00254"></a>00254 
-<a name="l00256"></a><a class="code" href="classbdm_1_1multiBM.html">00256</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="l00257"></a>00257 <span class="keyword">protected</span>:
-<a name="l00259"></a><a class="code" href="classbdm_1_1multiBM.html#9ecc6878abbd20eb8d8e43b6ab3f941a">00259</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="l00261"></a><a class="code" href="classbdm_1_1multiBM.html#044263356944c92209eecd39a5187d25">00261</a>         vec &amp;<a class="code" href="classbdm_1_1multiBM.html#044263356944c92209eecd39a5187d25" title="Pointer inside est to sufficient statistics.">beta</a>;
-<a name="l00262"></a>00262 <span class="keyword">public</span>:
-<a name="l00264"></a><a class="code" href="classbdm_1_1multiBM.html#c4dd6d9522a8a605776d21bac9bd9daf">00264</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() ) {<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>();}<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="l00266"></a><a class="code" href="classbdm_1_1multiBM.html#c4378cf8037f6bed29c74eea63344b31">00266</a>         <a class="code" href="classbdm_1_1multiBM.html#c4dd6d9522a8a605776d21bac9bd9daf" title="Default 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="l00268"></a><a class="code" href="classbdm_1_1multiBM.html#dbe6b90d410dc062a233d1dc09eeba52">00268</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="l00269"></a><a class="code" href="classbdm_1_1multiBM.html#1e4bf41b61937fd80f34049742e23f95">00269</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="l00270"></a>00270                 <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="l00271"></a>00271                 <a class="code" href="classbdm_1_1multiBM.html#044263356944c92209eecd39a5187d25" title="Pointer inside est to sufficient statistics.">beta</a>+=dt;
-<a name="l00272"></a>00272                 <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="l00273"></a>00273         }
-<a name="l00274"></a><a class="code" href="classbdm_1_1multiBM.html#e157b607c1e3fa91d42aeea44458e2bf">00274</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="l00275"></a>00275                 <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="l00276"></a>00276                 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="l00277"></a>00277 
-<a name="l00278"></a>00278                 <span class="keywordtype">double</span> lll;
-<a name="l00279"></a>00279                 <span class="keywordflow">if</span> ( <a class="code" href="classbdm_1_1BMEF.html#1331865e10fb1ccef65bb4c47fa3be64" title="forgetting factor">frg</a>&lt;1.0 )
-<a name="l00280"></a>00280                         {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="l00281"></a>00281                 <span class="keywordflow">else</span>
-<a name="l00282"></a>00282                         <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="l00283"></a>00283                         <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="l00284"></a>00284 
-<a name="l00285"></a>00285                 beta+=dt;
-<a name="l00286"></a>00286                 <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="l00287"></a>00287         }
-<a name="l00288"></a><a class="code" href="classbdm_1_1multiBM.html#aaeb18c989088feb8d26d300e4971732">00288</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="l00289"></a>00289                 <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="l00290"></a>00290                 <span class="comment">// sum(beta) should be equal to sum(B.beta)</span>
-<a name="l00291"></a>00291                 <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="l00292"></a>00292                 <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="l00293"></a>00293                 <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="l00294"></a>00294         }
-<a name="l00295"></a>00295         <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; _epdf()<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="l00296"></a>00296         <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="l00297"></a>00297         <span class="keywordtype">void</span> set_parameters ( <span class="keyword">const</span> vec &amp;beta0 ) {
-<a name="l00298"></a>00298                 <a class="code" href="classbdm_1_1multiBM.html#9ecc6878abbd20eb8d8e43b6ab3f941a" title="Conjugate prior and posterior.">est</a>.set_parameters ( beta0 );
-<a name="l00299"></a>00299                 <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="l00300"></a>00300         }
-<a name="l00301"></a>00301 };
-<a name="l00302"></a>00302 
-<a name="l00312"></a><a class="code" href="classbdm_1_1egamma.html">00312</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="l00313"></a>00313 <span class="keyword">protected</span>:
-<a name="l00315"></a><a class="code" href="classbdm_1_1egamma.html#0901ec983e66b8337aaa506e13b122fa">00315</a>         vec <a class="code" href="classbdm_1_1egamma.html#0901ec983e66b8337aaa506e13b122fa" title="Vector .">alpha</a>;
-<a name="l00317"></a><a class="code" href="classbdm_1_1egamma.html#457bfb1ccb2057df85073e519a15ccc1">00317</a>         vec <a class="code" href="classbdm_1_1egamma.html#457bfb1ccb2057df85073e519a15ccc1" title="Vector .">beta</a>;
-<a name="l00318"></a>00318 <span class="keyword">public</span> :
-<a name="l00320"></a><a class="code" href="classbdm_1_1egamma.html#131197f3dfad99355a01d30903279653">00320</a>         <a class="code" href="classbdm_1_1egamma.html#131197f3dfad99355a01d30903279653" title="Default constructor.">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>(), <a class="code" href="classbdm_1_1egamma.html#457bfb1ccb2057df85073e519a15ccc1" title="Vector .">beta</a>() {};
-<a name="l00322"></a><a class="code" href="classbdm_1_1egamma.html#749f82293ff23a8319c1bf52489d2ed2">00322</a>         <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1egamma.html#749f82293ff23a8319c1bf52489d2ed2" title="Sets parameters.">set_parameters</a> ( <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="l00323"></a>00323         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="l00325"></a>00325 <span class="comment">//      mat sample ( int N ) const;</span>
-<a name="l00326"></a>00326         <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="l00327"></a>00327         <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="l00329"></a><a class="code" href="classbdm_1_1egamma.html#0865cb3d6339fdc7410806cf70a329ed">00329</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="l00330"></a>00330         vec&amp; _beta() {<span class="keywordflow">return</span> <a class="code" href="classbdm_1_1egamma.html#457bfb1ccb2057df85073e519a15ccc1" title="Vector .">beta</a>;}
-<a name="l00331"></a><a class="code" href="classbdm_1_1egamma.html#49d256c42cce14c6faa56ec242b57e85">00331</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>,<a class="code" href="classbdm_1_1egamma.html#457bfb1ccb2057df85073e519a15ccc1" title="Vector .">beta</a> );}
-<a name="l00332"></a><a class="code" href="classbdm_1_1egamma.html#36986cc01917cd0796fadc17125bdec1">00332</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 ( <a class="code" href="classbdm_1_1egamma.html#457bfb1ccb2057df85073e519a15ccc1" title="Vector .">beta</a>,<a class="code" href="classbdm_1_1egamma.html#457bfb1ccb2057df85073e519a15ccc1" title="Vector .">beta</a> ) ); }
-<a name="l00333"></a>00333 };
-<a name="l00334"></a>00334 
-<a name="l00349"></a><a class="code" href="classbdm_1_1eigamma.html">00349</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_1eEF.html" title="General conjugate exponential family posterior density.">eEF</a> {
-<a name="l00350"></a>00350 <span class="keyword">protected</span>:
-<a name="l00352"></a><a class="code" href="classbdm_1_1eigamma.html#3e5c03201f073033a7db894fa15ddb96">00352</a>         <a class="code" href="classbdm_1_1egamma.html" title="Gamma posterior density.">egamma</a> <a class="code" href="classbdm_1_1eigamma.html#3e5c03201f073033a7db894fa15ddb96" title="internal egamma">eg</a>;
-<a name="l00354"></a><a class="code" href="classbdm_1_1eigamma.html#494fafd76d9448395efe160c9ba11ab6">00354</a>         vec &amp;<a class="code" href="classbdm_1_1eigamma.html#494fafd76d9448395efe160c9ba11ab6" title="Vector .">alpha</a>;
-<a name="l00356"></a><a class="code" href="classbdm_1_1eigamma.html#e32ff12ba6b2fdc22068c35dace23fa6">00356</a>         vec &amp;<a class="code" href="classbdm_1_1eigamma.html#e32ff12ba6b2fdc22068c35dace23fa6" title="Vector  (in fact it is 1/beta as used in definition of iG).">beta</a>;
-<a name="l00357"></a>00357 <span class="keyword">public</span> :
-<a name="l00359"></a><a class="code" href="classbdm_1_1eigamma.html#b496396f3511ce87d7ae3830ba94262c">00359</a>         <a class="code" href="classbdm_1_1eigamma.html#b496396f3511ce87d7ae3830ba94262c" title="Default constructor.">eigamma</a> ( ) :<a class="code" href="classbdm_1_1eEF.html" title="General conjugate exponential family posterior density.">eEF</a> ( ), <a class="code" href="classbdm_1_1eigamma.html#3e5c03201f073033a7db894fa15ddb96" title="internal egamma">eg</a>(),<a class="code" href="classbdm_1_1eigamma.html#494fafd76d9448395efe160c9ba11ab6" title="Vector .">alpha</a> ( <a class="code" href="classbdm_1_1eigamma.html#3e5c03201f073033a7db894fa15ddb96" title="internal egamma">eg</a>._alpha() ), <a class="code" href="classbdm_1_1eigamma.html#e32ff12ba6b2fdc22068c35dace23fa6" title="Vector  (in fact it is 1/beta as used in definition of iG).">beta</a> ( <a class="code" href="classbdm_1_1eigamma.html#3e5c03201f073033a7db894fa15ddb96" title="internal egamma">eg</a>._beta() ) {};
-<a name="l00361"></a><a class="code" href="classbdm_1_1eigamma.html#09e616c95f31acddf7dfef96d1c5d645">00361</a>         <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1eigamma.html#09e616c95f31acddf7dfef96d1c5d645" title="Sets parameters.">set_parameters</a> ( <span class="keyword">const</span> vec &amp;a, <span class="keyword">const</span> vec &amp;b ) {<a class="code" href="classbdm_1_1eigamma.html#494fafd76d9448395efe160c9ba11ab6" title="Vector .">alpha</a>=a,<a class="code" href="classbdm_1_1eigamma.html#e32ff12ba6b2fdc22068c35dace23fa6" title="Vector  (in fact it is 1/beta as used in definition of iG).">beta</a>=b;};
-<a name="l00362"></a><a class="code" href="classbdm_1_1eigamma.html#3aff7bf25ddac27731c60826fcfd878f">00362</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#3e5c03201f073033a7db894fa15ddb96" title="internal egamma">eg</a>.<a class="code" href="classbdm_1_1egamma.html#6ed82f0fd05f6002487256d8e75a0bbd" title="Returns a sample,  from density .">sample</a>();};
-<a name="l00364"></a>00364 <span class="comment">//      mat sample ( int N ) const;</span>
-<a name="l00365"></a><a class="code" href="classbdm_1_1eigamma.html#9e26c80c8e6708bfcf2e684958af6f91">00365</a>         <span class="keywordtype">double</span> <a class="code" href="classbdm_1_1eigamma.html#9e26c80c8e6708bfcf2e684958af6f91" title="TODO: is it used anywhere?">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_1eigamma.html#3e5c03201f073033a7db894fa15ddb96" title="internal egamma">eg</a>.<a class="code" href="classbdm_1_1egamma.html#a8e11e5a580ff42a1b205974c60768c6" title="TODO: is it used anywhere?">evallog</a> ( val );};
-<a name="l00366"></a><a class="code" href="classbdm_1_1eigamma.html#a52ac6d523e2fe05642d1f50fe66aec2">00366</a>         <span class="keywordtype">double</span> <a class="code" href="classbdm_1_1eigamma.html#a52ac6d523e2fe05642d1f50fe66aec2" title="logarithm of the normalizing constant, ">lognc</a> ()<span class="keyword"> const </span>{<span class="keywordflow">return</span> <a class="code" href="classbdm_1_1eigamma.html#3e5c03201f073033a7db894fa15ddb96" title="internal egamma">eg</a>.<a class="code" href="classbdm_1_1egamma.html#9a66cbd100e8520c769ccb3c451f86f8" title="logarithm of the normalizing constant, ">lognc</a>();};
-<a name="l00368"></a><a class="code" href="classbdm_1_1eigamma.html#46cecb295edbabd28120cb0f6f572bcb">00368</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_1eigamma.html#e32ff12ba6b2fdc22068c35dace23fa6" title="Vector  (in fact it is 1/beta as used in definition of iG).">beta</a>,<a class="code" href="classbdm_1_1eigamma.html#494fafd76d9448395efe160c9ba11ab6" title="Vector .">alpha</a>-1 );}
-<a name="l00369"></a><a class="code" href="classbdm_1_1eigamma.html#c2c696f8c668e9f65392c9449f6a5133">00369</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_1eigamma.html#494fafd76d9448395efe160c9ba11ab6" title="Vector .">alpha</a>-2 );}
-<a name="l00370"></a>00370         vec&amp; _alpha() {<span class="keywordflow">return</span> <a class="code" href="classbdm_1_1eigamma.html#494fafd76d9448395efe160c9ba11ab6" title="Vector .">alpha</a>;}
-<a name="l00371"></a>00371         vec&amp; _beta() {<span class="keywordflow">return</span> <a class="code" href="classbdm_1_1eigamma.html#e32ff12ba6b2fdc22068c35dace23fa6" title="Vector  (in fact it is 1/beta as used in definition of iG).">beta</a>;}
-<a name="l00372"></a>00372 };
-<a name="l00373"></a>00373 <span class="comment">/*</span>
-<a name="l00375"></a>00375 <span class="comment">class emix : public epdf {</span>
-<a name="l00376"></a>00376 <span class="comment">protected:</span>
-<a name="l00377"></a>00377 <span class="comment">        int n;</span>
-<a name="l00378"></a>00378 <span class="comment">        vec &amp;w;</span>
-<a name="l00379"></a>00379 <span class="comment">        Array&lt;epdf*&gt; Coms;</span>
-<a name="l00380"></a>00380 <span class="comment">public:</span>
-<a name="l00382"></a>00382 <span class="comment">        emix ( const RV &amp;rv, vec &amp;w0): epdf(rv), n(w0.length()), w(w0), Coms(n) {};</span>
-<a name="l00383"></a>00383 <span class="comment">        void set_parameters( int &amp;i, double wi, epdf* ep){w(i)=wi;Coms(i)=ep;}</span>
-<a name="l00384"></a>00384 <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="l00385"></a>00385 <span class="comment">        vec sample() {it_error ( "Not implemented" );return 0;}</span>
-<a name="l00386"></a>00386 <span class="comment">};</span>
-<a name="l00387"></a>00387 <span class="comment">*/</span>
-<a name="l00388"></a>00388 
-<a name="l00390"></a>00390 
-<a name="l00391"></a><a class="code" href="classbdm_1_1euni.html">00391</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="l00392"></a>00392 <span class="keyword">protected</span>:
-<a name="l00394"></a><a class="code" href="classbdm_1_1euni.html#ff7ce6a2ef5ef0015bbd1398bed12f32">00394</a>         vec <a class="code" href="classbdm_1_1euni.html#ff7ce6a2ef5ef0015bbd1398bed12f32" title="lower bound on support">low</a>;
-<a name="l00396"></a><a class="code" href="classbdm_1_1euni.html#cfad2dea4a62db6872bda8abd75f0de1">00396</a>         vec <a class="code" href="classbdm_1_1euni.html#cfad2dea4a62db6872bda8abd75f0de1" title="upper bound on support">high</a>;
-<a name="l00398"></a><a class="code" href="classbdm_1_1euni.html#d3c27e331f90c754d80228108de8ed4c">00398</a>         vec <a class="code" href="classbdm_1_1euni.html#d3c27e331f90c754d80228108de8ed4c" title="internal">distance</a>;
-<a name="l00400"></a><a class="code" href="classbdm_1_1euni.html#31bb13e8449a8eff35246d46dae35c20">00400</a>         <span class="keywordtype">double</span> <a class="code" href="classbdm_1_1euni.html#31bb13e8449a8eff35246d46dae35c20" title="normalizing coefficients">nk</a>;
-<a name="l00402"></a><a class="code" href="classbdm_1_1euni.html#3e63be48dd58659663ca60cd18700476">00402</a>         <span class="keywordtype">double</span> <a class="code" href="classbdm_1_1euni.html#3e63be48dd58659663ca60cd18700476" title="cache of log( nk )">lnk</a>;
-<a name="l00403"></a>00403 <span class="keyword">public</span>:
-<a name="l00405"></a><a class="code" href="classbdm_1_1euni.html#8887b088e3ab0b01e96b88237d707b97">00405</a>         <a class="code" href="classbdm_1_1euni.html#8887b088e3ab0b01e96b88237d707b97" title="Defualt constructor.">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="l00406"></a>00406         <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="l00407"></a><a class="code" href="classbdm_1_1euni.html#caa07b8307bd793d5339d6583e0aba81">00407</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="l00408"></a><a class="code" href="classbdm_1_1euni.html#fc5df80359ead2918384b2004ce67194">00408</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="l00409"></a>00409                 vec smp ( <a class="code" href="classbdm_1_1epdf.html#16adac20ec7fe07e1ea0b27d917788ce" title="dimension of the random variable">dim</a> );
-<a name="l00410"></a>00410 <span class="preprocessor">#pragma omp critical</span>
-<a name="l00411"></a>00411 <span class="preprocessor"></span>                <a class="code" href="namespacebdm.html#96288dbda6916cd442af735f66a9f40b" title="Global Uniform_RNG.">UniRNG</a>.sample_vector ( <a class="code" href="classbdm_1_1epdf.html#16adac20ec7fe07e1ea0b27d917788ce" title="dimension of the random variable">dim</a> ,smp );
-<a name="l00412"></a>00412                 <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="l00413"></a>00413         }
-<a name="l00415"></a><a class="code" href="classbdm_1_1euni.html#8e130b323c62b42f1699537f99af6f09">00415</a>         <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1euni.html#8e130b323c62b42f1699537f99af6f09" title="set values of low and high ">set_parameters</a> ( <span class="keyword">const</span> vec &amp;low0, <span class="keyword">const</span> vec &amp;high0 ) {
-<a name="l00416"></a>00416                 <a class="code" href="classbdm_1_1euni.html#d3c27e331f90c754d80228108de8ed4c" title="internal">distance</a> = high0-low0;
-<a name="l00417"></a>00417                 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="l00418"></a>00418                 <a class="code" href="classbdm_1_1euni.html#ff7ce6a2ef5ef0015bbd1398bed12f32" title="lower bound on support">low</a> = low0;
-<a name="l00419"></a>00419                 <a class="code" href="classbdm_1_1euni.html#cfad2dea4a62db6872bda8abd75f0de1" title="upper bound on support">high</a> = high0;
-<a name="l00420"></a>00420                 <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="l00421"></a>00421                 <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="l00422"></a>00422                 <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="l00423"></a>00423         }
-<a name="l00424"></a><a class="code" href="classbdm_1_1euni.html#46caa8c13aba2e6228f964208918b226">00424</a>         vec <a class="code" href="classbdm_1_1euni.html#46caa8c13aba2e6228f964208918b226" title="return expected value">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="l00425"></a><a class="code" href="classbdm_1_1euni.html#951f932155111f6053c980f672b4c22c">00425</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="l00426"></a>00426 };
-<a name="l00427"></a>00427 
+<a name="l00245"></a><a class="code" href="classbdm_1_1eDirich.html#279a99f6266c82fe2273e83841f19eb2">00245</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="l00246"></a>00246                 <span class="keywordtype">double</span> tmp;
+<a name="l00247"></a>00247                 <span class="keywordtype">double</span> gam=sum ( <a class="code" href="classbdm_1_1eDirich.html#f25886a49b4667af61245de81c83b5d2" title="sufficient statistics">beta</a> );
+<a name="l00248"></a>00248                 <span class="keywordtype">double</span> lgb=0.0;
+<a name="l00249"></a>00249                 <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="l00250"></a>00250                 tmp= lgb-lgamma ( gam );
+<a name="l00251"></a>00251                 it_assert_debug ( std::isfinite ( tmp ),<span class="stringliteral">"Infinite value"</span> );
+<a name="l00252"></a>00252                 <span class="keywordflow">return</span> tmp;
+<a name="l00253"></a>00253         };
+<a name="l00255"></a><a class="code" href="classbdm_1_1eDirich.html#175e0add26d2105c28d8121eefb9e324">00255</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="l00257"></a>00257 };
+<a name="l00258"></a>00258 
+<a name="l00260"></a><a class="code" href="classbdm_1_1multiBM.html">00260</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="l00261"></a>00261 <span class="keyword">protected</span>:
+<a name="l00263"></a><a class="code" href="classbdm_1_1multiBM.html#9ecc6878abbd20eb8d8e43b6ab3f941a">00263</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="l00265"></a><a class="code" href="classbdm_1_1multiBM.html#044263356944c92209eecd39a5187d25">00265</a>         vec &amp;<a class="code" href="classbdm_1_1multiBM.html#044263356944c92209eecd39a5187d25" title="Pointer inside est to sufficient statistics.">beta</a>;
+<a name="l00266"></a>00266 <span class="keyword">public</span>:
+<a name="l00268"></a><a class="code" href="classbdm_1_1multiBM.html#c4dd6d9522a8a605776d21bac9bd9daf">00268</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() ) {<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>();}<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="l00270"></a><a class="code" href="classbdm_1_1multiBM.html#c4378cf8037f6bed29c74eea63344b31">00270</a>         <a class="code" href="classbdm_1_1multiBM.html#c4dd6d9522a8a605776d21bac9bd9daf" title="Default 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="l00272"></a><a class="code" href="classbdm_1_1multiBM.html#dbe6b90d410dc062a233d1dc09eeba52">00272</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="l00273"></a><a class="code" href="classbdm_1_1multiBM.html#1e4bf41b61937fd80f34049742e23f95">00273</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="l00274"></a>00274                 <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="l00275"></a>00275                 <a class="code" href="classbdm_1_1multiBM.html#044263356944c92209eecd39a5187d25" title="Pointer inside est to sufficient statistics.">beta</a>+=dt;
+<a name="l00276"></a>00276                 <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="l00277"></a>00277         }
+<a name="l00278"></a><a class="code" href="classbdm_1_1multiBM.html#e157b607c1e3fa91d42aeea44458e2bf">00278</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="l00279"></a>00279                 <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="l00280"></a>00280                 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="l00281"></a>00281 
+<a name="l00282"></a>00282                 <span class="keywordtype">double</span> lll;
+<a name="l00283"></a>00283                 <span class="keywordflow">if</span> ( <a class="code" href="classbdm_1_1BMEF.html#1331865e10fb1ccef65bb4c47fa3be64" title="forgetting factor">frg</a>&lt;1.0 )
+<a name="l00284"></a>00284                         {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="l00285"></a>00285                 <span class="keywordflow">else</span>
+<a name="l00286"></a>00286                         <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="l00287"></a>00287                         <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="l00288"></a>00288 
+<a name="l00289"></a>00289                 beta+=dt;
+<a name="l00290"></a>00290                 <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="l00291"></a>00291         }
+<a name="l00292"></a><a class="code" href="classbdm_1_1multiBM.html#aaeb18c989088feb8d26d300e4971732">00292</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="l00293"></a>00293                 <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="l00294"></a>00294                 <span class="comment">// sum(beta) should be equal to sum(B.beta)</span>
+<a name="l00295"></a>00295                 <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="l00296"></a>00296                 <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="l00297"></a>00297                 <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="l00298"></a>00298         }
+<a name="l00299"></a>00299         <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="l00300"></a>00300         <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="l00301"></a>00301         <span class="keywordtype">void</span> set_parameters ( <span class="keyword">const</span> vec &amp;beta0 ) {
+<a name="l00302"></a>00302                 <a class="code" href="classbdm_1_1multiBM.html#9ecc6878abbd20eb8d8e43b6ab3f941a" title="Conjugate prior and posterior.">est</a>.set_parameters ( beta0 );
+<a name="l00303"></a>00303                 <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="l00304"></a>00304         }
+<a name="l00305"></a>00305 };
+<a name="l00306"></a>00306 
+<a name="l00316"></a><a class="code" href="classbdm_1_1egamma.html">00316</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="l00317"></a>00317 <span class="keyword">protected</span>:
+<a name="l00319"></a><a class="code" href="classbdm_1_1egamma.html#0901ec983e66b8337aaa506e13b122fa">00319</a>         vec <a class="code" href="classbdm_1_1egamma.html#0901ec983e66b8337aaa506e13b122fa" title="Vector .">alpha</a>;
+<a name="l00321"></a><a class="code" href="classbdm_1_1egamma.html#457bfb1ccb2057df85073e519a15ccc1">00321</a>         vec <a class="code" href="classbdm_1_1egamma.html#457bfb1ccb2057df85073e519a15ccc1" title="Vector .">beta</a>;
+<a name="l00322"></a>00322 <span class="keyword">public</span> :
+<a name="l00325"></a>00325         <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>(), <a class="code" href="classbdm_1_1egamma.html#457bfb1ccb2057df85073e519a15ccc1" title="Vector .">beta</a>() {};
+<a name="l00326"></a>00326         <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="l00327"></a>00327         <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="l00329"></a>00329 
+<a name="l00330"></a>00330         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="l00332"></a>00332 <span class="comment">//      mat sample ( int N ) const;</span>
+<a name="l00333"></a>00333         <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="l00334"></a>00334         <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="l00336"></a><a class="code" href="classbdm_1_1egamma.html#0865cb3d6339fdc7410806cf70a329ed">00336</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="l00337"></a>00337         vec&amp; _beta() {<span class="keywordflow">return</span> beta;}
+<a name="l00338"></a><a class="code" href="classbdm_1_1egamma.html#49d256c42cce14c6faa56ec242b57e85">00338</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="l00339"></a><a class="code" href="classbdm_1_1egamma.html#36986cc01917cd0796fadc17125bdec1">00339</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="l00340"></a>00340 };
+<a name="l00341"></a>00341 
+<a name="l00356"></a><a class="code" href="classbdm_1_1eigamma.html">00356</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_1eEF.html" title="General conjugate exponential family posterior density.">eEF</a> {
+<a name="l00357"></a>00357 <span class="keyword">protected</span>:
+<a name="l00359"></a><a class="code" href="classbdm_1_1eigamma.html#3e5c03201f073033a7db894fa15ddb96">00359</a>         <a class="code" href="classbdm_1_1egamma.html" title="Gamma posterior density.">egamma</a> <a class="code" href="classbdm_1_1eigamma.html#3e5c03201f073033a7db894fa15ddb96" title="internal egamma">eg</a>;
+<a name="l00361"></a><a class="code" href="classbdm_1_1eigamma.html#494fafd76d9448395efe160c9ba11ab6">00361</a>         vec &amp;<a class="code" href="classbdm_1_1eigamma.html#494fafd76d9448395efe160c9ba11ab6" title="Vector .">alpha</a>;
+<a name="l00363"></a><a class="code" href="classbdm_1_1eigamma.html#e32ff12ba6b2fdc22068c35dace23fa6">00363</a>         vec &amp;<a class="code" href="classbdm_1_1eigamma.html#e32ff12ba6b2fdc22068c35dace23fa6" title="Vector  (in fact it is 1/beta as used in definition of iG).">beta</a>;
+<a name="l00364"></a>00364 <span class="keyword">public</span> :
+<a name="l00367"></a>00367         <a class="code" href="classbdm_1_1eigamma.html" title="Inverse-Gamma posterior density.">eigamma</a> ( ) :<a class="code" href="classbdm_1_1eEF.html" title="General conjugate exponential family posterior density.">eEF</a> ( ), <a class="code" href="classbdm_1_1eigamma.html#3e5c03201f073033a7db894fa15ddb96" title="internal egamma">eg</a>(),<a class="code" href="classbdm_1_1eigamma.html#494fafd76d9448395efe160c9ba11ab6" title="Vector .">alpha</a> ( <a class="code" href="classbdm_1_1eigamma.html#3e5c03201f073033a7db894fa15ddb96" title="internal egamma">eg</a>._alpha() ), <a class="code" href="classbdm_1_1eigamma.html#e32ff12ba6b2fdc22068c35dace23fa6" title="Vector  (in fact it is 1/beta as used in definition of iG).">beta</a> ( <a class="code" href="classbdm_1_1eigamma.html#3e5c03201f073033a7db894fa15ddb96" title="internal egamma">eg</a>._beta() ) {};
+<a name="l00368"></a>00368         <a class="code" href="classbdm_1_1eigamma.html" title="Inverse-Gamma posterior density.">eigamma</a> ( <span class="keyword">const</span> vec &amp;a, <span class="keyword">const</span> vec &amp;b ):<a class="code" href="classbdm_1_1eEF.html" title="General conjugate exponential family posterior density.">eEF</a> ( ), <a class="code" href="classbdm_1_1eigamma.html#3e5c03201f073033a7db894fa15ddb96" title="internal egamma">eg</a>(),<a class="code" href="classbdm_1_1eigamma.html#494fafd76d9448395efe160c9ba11ab6" title="Vector .">alpha</a> ( <a class="code" href="classbdm_1_1eigamma.html#3e5c03201f073033a7db894fa15ddb96" title="internal egamma">eg</a>._alpha() ), <a class="code" href="classbdm_1_1eigamma.html#e32ff12ba6b2fdc22068c35dace23fa6" title="Vector  (in fact it is 1/beta as used in definition of iG).">beta</a> ( <a class="code" href="classbdm_1_1eigamma.html#3e5c03201f073033a7db894fa15ddb96" title="internal egamma">eg</a>._beta() ) {<a class="code" href="classbdm_1_1eigamma.html#3e5c03201f073033a7db894fa15ddb96" title="internal egamma">eg</a>.<a class="code" href="classbdm_1_1egamma.html#749f82293ff23a8319c1bf52489d2ed2">set_parameters</a> ( a,b );};
+<a name="l00369"></a>00369         <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_1eigamma.html#3e5c03201f073033a7db894fa15ddb96" title="internal egamma">eg</a>.set_parameters ( a,b );};
+<a name="l00371"></a>00371 
+<a name="l00372"></a><a class="code" href="classbdm_1_1eigamma.html#3aff7bf25ddac27731c60826fcfd878f">00372</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#3e5c03201f073033a7db894fa15ddb96" title="internal egamma">eg</a>.<a class="code" href="classbdm_1_1egamma.html#6ed82f0fd05f6002487256d8e75a0bbd" title="Returns a sample,  from density .">sample</a>();};
+<a name="l00374"></a>00374 <span class="comment">//      mat sample ( int N ) const;</span>
+<a name="l00375"></a><a class="code" href="classbdm_1_1eigamma.html#9e26c80c8e6708bfcf2e684958af6f91">00375</a>         <span class="keywordtype">double</span> <a class="code" href="classbdm_1_1eigamma.html#9e26c80c8e6708bfcf2e684958af6f91" title="TODO: is it used anywhere?">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_1eigamma.html#3e5c03201f073033a7db894fa15ddb96" title="internal egamma">eg</a>.<a class="code" href="classbdm_1_1egamma.html#a8e11e5a580ff42a1b205974c60768c6" title="TODO: is it used anywhere?">evallog</a> ( val );};
+<a name="l00376"></a><a class="code" href="classbdm_1_1eigamma.html#a52ac6d523e2fe05642d1f50fe66aec2">00376</a>         <span class="keywordtype">double</span> <a class="code" href="classbdm_1_1eigamma.html#a52ac6d523e2fe05642d1f50fe66aec2" title="logarithm of the normalizing constant, ">lognc</a> ()<span class="keyword"> const </span>{<span class="keywordflow">return</span> <a class="code" href="classbdm_1_1eigamma.html#3e5c03201f073033a7db894fa15ddb96" title="internal egamma">eg</a>.<a class="code" href="classbdm_1_1egamma.html#9a66cbd100e8520c769ccb3c451f86f8" title="logarithm of the normalizing constant, ">lognc</a>();};
+<a name="l00378"></a><a class="code" href="classbdm_1_1eigamma.html#46cecb295edbabd28120cb0f6f572bcb">00378</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_1eigamma.html#e32ff12ba6b2fdc22068c35dace23fa6" title="Vector  (in fact it is 1/beta as used in definition of iG).">beta</a>,<a class="code" href="classbdm_1_1eigamma.html#494fafd76d9448395efe160c9ba11ab6" title="Vector .">alpha</a>-1 );}
+<a name="l00379"></a><a class="code" href="classbdm_1_1eigamma.html#c2c696f8c668e9f65392c9449f6a5133">00379</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_1eigamma.html#494fafd76d9448395efe160c9ba11ab6" title="Vector .">alpha</a>-2 );}
+<a name="l00380"></a>00380         vec&amp; _alpha() {<span class="keywordflow">return</span> <a class="code" href="classbdm_1_1eigamma.html#494fafd76d9448395efe160c9ba11ab6" title="Vector .">alpha</a>;}
+<a name="l00381"></a>00381         vec&amp; _beta() {<span class="keywordflow">return</span> <a class="code" href="classbdm_1_1eigamma.html#e32ff12ba6b2fdc22068c35dace23fa6" title="Vector  (in fact it is 1/beta as used in definition of iG).">beta</a>;}
+<a name="l00382"></a>00382 };
+<a name="l00383"></a>00383 <span class="comment">/*</span>
+<a name="l00385"></a>00385 <span class="comment">class emix : public epdf {</span>
+<a name="l00386"></a>00386 <span class="comment">protected:</span>
+<a name="l00387"></a>00387 <span class="comment">        int n;</span>
+<a name="l00388"></a>00388 <span class="comment">        vec &amp;w;</span>
+<a name="l00389"></a>00389 <span class="comment">        Array&lt;epdf*&gt; Coms;</span>
+<a name="l00390"></a>00390 <span class="comment">public:</span>
+<a name="l00392"></a>00392 <span class="comment">        emix ( const RV &amp;rv, vec &amp;w0): epdf(rv), n(w0.length()), w(w0), Coms(n) {};</span>
+<a name="l00393"></a>00393 <span class="comment">        void set_parameters( int &amp;i, double wi, epdf* ep){w(i)=wi;Coms(i)=ep;}</span>
+<a name="l00394"></a>00394 <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="l00395"></a>00395 <span class="comment">        vec sample() {it_error ( "Not implemented" );return 0;}</span>
+<a name="l00396"></a>00396 <span class="comment">};</span>
+<a name="l00397"></a>00397 <span class="comment">*/</span>
+<a name="l00398"></a>00398 
+<a name="l00400"></a>00400 
+<a name="l00401"></a><a class="code" href="classbdm_1_1euni.html">00401</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="l00402"></a>00402 <span class="keyword">protected</span>:
+<a name="l00404"></a><a class="code" href="classbdm_1_1euni.html#ff7ce6a2ef5ef0015bbd1398bed12f32">00404</a>         vec <a class="code" href="classbdm_1_1euni.html#ff7ce6a2ef5ef0015bbd1398bed12f32" title="lower bound on support">low</a>;
+<a name="l00406"></a><a class="code" href="classbdm_1_1euni.html#cfad2dea4a62db6872bda8abd75f0de1">00406</a>         vec <a class="code" href="classbdm_1_1euni.html#cfad2dea4a62db6872bda8abd75f0de1" title="upper bound on support">high</a>;
+<a name="l00408"></a><a class="code" href="classbdm_1_1euni.html#d3c27e331f90c754d80228108de8ed4c">00408</a>         vec <a class="code" href="classbdm_1_1euni.html#d3c27e331f90c754d80228108de8ed4c" title="internal">distance</a>;
+<a name="l00410"></a><a class="code" href="classbdm_1_1euni.html#31bb13e8449a8eff35246d46dae35c20">00410</a>         <span class="keywordtype">double</span> <a class="code" href="classbdm_1_1euni.html#31bb13e8449a8eff35246d46dae35c20" title="normalizing coefficients">nk</a>;
+<a name="l00412"></a><a class="code" href="classbdm_1_1euni.html#3e63be48dd58659663ca60cd18700476">00412</a>         <span class="keywordtype">double</span> <a class="code" href="classbdm_1_1euni.html#3e63be48dd58659663ca60cd18700476" title="cache of log( nk )">lnk</a>;
+<a name="l00413"></a>00413 <span class="keyword">public</span>:
+<a name="l00416"></a>00416         <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="l00417"></a>00417         <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="l00418"></a>00418         <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="l00419"></a>00419                 <a class="code" href="classbdm_1_1euni.html#d3c27e331f90c754d80228108de8ed4c" title="internal">distance</a> = high0-low0;
+<a name="l00420"></a>00420                 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="l00421"></a>00421                 <a class="code" href="classbdm_1_1euni.html#ff7ce6a2ef5ef0015bbd1398bed12f32" title="lower bound on support">low</a> = low0;
+<a name="l00422"></a>00422                 <a class="code" href="classbdm_1_1euni.html#cfad2dea4a62db6872bda8abd75f0de1" title="upper bound on support">high</a> = high0;
+<a name="l00423"></a>00423                 <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="l00424"></a>00424                 <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="l00425"></a>00425                 <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="l00426"></a>00426         }
 <a name="l00428"></a>00428 
-<a name="l00434"></a>00434 <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt;
-<a name="l00435"></a><a class="code" href="classbdm_1_1mlnorm.html">00435</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="l00436"></a>00436 <span class="keyword">protected</span>:
-<a name="l00438"></a><a class="code" href="classbdm_1_1mlnorm.html#150ad6acb223b0a0abeaf92346686dcd">00438</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="l00439"></a>00439         mat A;
-<a name="l00440"></a>00440         vec mu_const;
-<a name="l00441"></a>00441         vec&amp; _mu; <span class="comment">//cached epdf.mu;</span>
-<a name="l00442"></a>00442 <span class="keyword">public</span>:
-<a name="l00444"></a>00444         <a class="code" href="classbdm_1_1mlnorm.html#2dcc06a3dd71f038efbe14dc34d937ae" title="Constructor.">mlnorm</a> ( );
-<a name="l00446"></a>00446         <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="l00447"></a>00447 <span class="comment">//      //!Generate one sample of the posterior</span>
-<a name="l00448"></a>00448 <span class="comment">//      vec samplecond (const vec &amp;cond, double &amp;lik );</span>
-<a name="l00449"></a>00449 <span class="comment">//      //!Generate matrix of samples of the posterior</span>
-<a name="l00450"></a>00450 <span class="comment">//      mat samplecond (const vec &amp;cond, vec &amp;lik, int n );</span>
-<a name="l00452"></a>00452 <span class="comment"></span>        <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1mlnorm.html#0dafc0196e7e07fd06dc6716e0e18bc2" title="Set value of rvc . Result of this operation is stored in epdf use function _ep to...">condition</a> ( <span class="keyword">const</span> vec &amp;cond );
-<a name="l00453"></a>00453 
-<a name="l00455"></a><a class="code" href="classbdm_1_1mlnorm.html#56e587952f94fcac6cfc999eae6dbced">00455</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="l00457"></a><a class="code" href="classbdm_1_1mlnorm.html#262a2a486bff585f34bb6a5005b02614">00457</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="l00459"></a><a class="code" href="classbdm_1_1mlnorm.html#78120ecd1c2b1d7e80124b4603504604">00459</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="l00460"></a>00460 
-<a name="l00461"></a>00461         <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_M&gt;
-<a name="l00462"></a>00462         <span class="keyword">friend</span> std::ostream &amp;operator&lt;&lt; ( std::ostream &amp;os,  mlnorm&lt;sq_M&gt; &amp;ml );
-<a name="l00463"></a>00463 };
-<a name="l00464"></a>00464 
-<a name="l00472"></a><a class="code" href="classbdm_1_1mlstudent.html">00472</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="l00473"></a>00473 <span class="keyword">protected</span>:
-<a name="l00474"></a>00474         <a class="code" href="classldmat.html" title="Matrix stored in LD form, (commonly known as UD).">ldmat</a> Lambda;
-<a name="l00475"></a>00475         <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="l00476"></a>00476         <a class="code" href="classldmat.html" title="Matrix stored in LD form, (commonly known as UD).">ldmat</a> Re;
-<a name="l00477"></a>00477 <span class="keyword">public</span>:
-<a name="l00478"></a>00478         <a class="code" href="classbdm_1_1mlstudent.html">mlstudent</a> ( ) :<a class="code" href="classbdm_1_1mlnorm.html" title="Normal distributed linear function with linear function of mean value;.">mlnorm&lt;ldmat&gt;</a> (),
-<a name="l00479"></a>00479                         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="l00480"></a>00480         <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="l00481"></a>00481                 it_assert_debug ( A0.rows() ==mu0.length(),<span class="stringliteral">""</span> );
-<a name="l00482"></a>00482                 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="l00483"></a>00483 
-<a name="l00484"></a>00484                 <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="l00485"></a>00485                 A = A0;
-<a name="l00486"></a>00486                 mu_const = mu0;
-<a name="l00487"></a>00487                 Re=R0;
-<a name="l00488"></a>00488                 Lambda = Lambda0;
-<a name="l00489"></a>00489         }
-<a name="l00490"></a><a class="code" href="classbdm_1_1mlstudent.html#efd37560585c8613897f30d3c2f58d0d">00490</a>         <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1mlstudent.html#efd37560585c8613897f30d3c2f58d0d" title="Set value of rvc . Result of this operation is stored in epdf use function _ep to...">condition</a> ( <span class="keyword">const</span> vec &amp;cond ) {
-<a name="l00491"></a>00491                 _mu = A*cond + mu_const;
-<a name="l00492"></a>00492                 <span class="keywordtype">double</span> zeta;
-<a name="l00493"></a>00493                 <span class="comment">//ugly hack!</span>
-<a name="l00494"></a>00494                 <span class="keywordflow">if</span> ( ( cond.length() +1 ) ==Lambda.<a class="code" href="group__math.html#g96dfb21865db4f5bd36fa70f9b0b1163" title="access function">rows</a>() ) {
-<a name="l00495"></a>00495                         zeta = Lambda.<a class="code" href="classldmat.html#d876c5f83e02b3e809b35c9de5068f14" title="Evaluates quadratic form ;.">invqform</a> ( <a class="code" href="namespacebdm.html#b9016687c0e874ca5cdcf75ae28811aa" title="Concat two random variables.">concat</a> ( cond, vec_1 ( 1.0 ) ) );
-<a name="l00496"></a>00496                 }
-<a name="l00497"></a>00497                 <span class="keywordflow">else</span> {
-<a name="l00498"></a>00498                         zeta = Lambda.<a class="code" href="classldmat.html#d876c5f83e02b3e809b35c9de5068f14" title="Evaluates quadratic form ;.">invqform</a> ( cond );
-<a name="l00499"></a>00499                 }
-<a name="l00500"></a>00500                 <a class="code" href="classbdm_1_1mlnorm.html#78120ecd1c2b1d7e80124b4603504604" title="access function">_R</a> = Re;
-<a name="l00501"></a>00501                 <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="l00502"></a>00502         };
-<a name="l00503"></a>00503 
-<a name="l00504"></a>00504 };
-<a name="l00514"></a><a class="code" href="classbdm_1_1mgamma.html">00514</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="l00515"></a>00515 <span class="keyword">protected</span>:
-<a name="l00517"></a><a class="code" href="classbdm_1_1mgamma.html#bdc9f1e9e03c09e91103fee269864438">00517</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="l00519"></a><a class="code" href="classbdm_1_1mgamma.html#b20cf88cca1fe9b0b8f2a412608bfd09">00519</a>         <span class="keywordtype">double</span> <a class="code" href="classbdm_1_1mgamma.html#b20cf88cca1fe9b0b8f2a412608bfd09" title="Constant .">k</a>;
-<a name="l00521"></a><a class="code" href="classbdm_1_1mgamma.html#3d95f4dde9214ff6dba265e18af60312">00521</a>         vec &amp;<a class="code" href="classbdm_1_1mgamma.html#3d95f4dde9214ff6dba265e18af60312" title="cache of epdf.beta">_beta</a>;
-<a name="l00522"></a>00522 
-<a name="l00523"></a>00523 <span class="keyword">public</span>:
-<a name="l00525"></a><a class="code" href="classbdm_1_1mgamma.html#1a9dc8661e5b214a8185d6e6b9956eb1">00525</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="l00527"></a>00527         <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1mgamma.html#0b486f7e52a3d8a39adbcbd325461c0d" 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> );
-<a name="l00528"></a><a class="code" href="classbdm_1_1mgamma.html#8996500f1885e39cde30221b20900bff">00528</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="l00529"></a>00529 };
-<a name="l00530"></a>00530 
-<a name="l00540"></a><a class="code" href="classbdm_1_1migamma.html">00540</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="l00541"></a>00541 <span class="keyword">protected</span>:
-<a name="l00543"></a><a class="code" href="classbdm_1_1migamma.html#a31b39d4179551b593c9e0d7d756783a">00543</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="l00545"></a><a class="code" href="classbdm_1_1migamma.html#dc56bc9da542e0103ec16b9be8e5e38c">00545</a>         <span class="keywordtype">double</span> <a class="code" href="classbdm_1_1migamma.html#dc56bc9da542e0103ec16b9be8e5e38c" title="Constant .">k</a>;
-<a name="l00547"></a><a class="code" href="classbdm_1_1migamma.html#c9847093da59a9ba0ebb68d2c592f5dc">00547</a>         vec &amp;<a class="code" href="classbdm_1_1migamma.html#c9847093da59a9ba0ebb68d2c592f5dc" title="cache of epdf.alpha">_alpha</a>;
-<a name="l00549"></a><a class="code" href="classbdm_1_1migamma.html#0d854c047001b5465cf1ba21f52904b5">00549</a>         vec &amp;<a class="code" href="classbdm_1_1migamma.html#0d854c047001b5465cf1ba21f52904b5" title="cache of epdf.beta">_beta</a>;
-<a name="l00550"></a>00550 
-<a name="l00551"></a>00551 <span class="keyword">public</span>:
-<a name="l00554"></a>00554         <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="l00555"></a>00555         <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="l00557"></a>00557 
-<a name="l00559"></a><a class="code" href="classbdm_1_1migamma.html#8b10ab922e2a7bae2fb6bb3efc7b6151">00559</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="l00560"></a>00560                 <a class="code" href="classbdm_1_1migamma.html#dc56bc9da542e0103ec16b9be8e5e38c" title="Constant .">k</a>=k0;
-<a name="l00561"></a>00561                 <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="l00562"></a>00562         };
-<a name="l00563"></a><a class="code" href="classbdm_1_1migamma.html#7a34b1e2e3aa2250d7c0ed7df1665b8c">00563</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="l00564"></a>00564                 <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="l00565"></a>00565         };
-<a name="l00566"></a>00566 };
-<a name="l00567"></a>00567 
-<a name="l00579"></a><a class="code" href="classbdm_1_1mgamma__fix.html">00579</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="l00580"></a>00580 <span class="keyword">protected</span>:
-<a name="l00582"></a><a class="code" href="classbdm_1_1mgamma__fix.html#1eb701506aabb2e6af007e487212d6fa">00582</a>         <span class="keywordtype">double</span> <a class="code" href="classbdm_1_1mgamma__fix.html#1eb701506aabb2e6af007e487212d6fa" title="parameter l">l</a>;
-<a name="l00584"></a><a class="code" href="classbdm_1_1mgamma__fix.html#018c6f901a04e419455308a07eb3b0b2">00584</a>         vec <a class="code" href="classbdm_1_1mgamma__fix.html#018c6f901a04e419455308a07eb3b0b2" title="reference vector">refl</a>;
-<a name="l00585"></a>00585 <span class="keyword">public</span>:
-<a name="l00587"></a><a class="code" href="classbdm_1_1mgamma__fix.html#9a31bc9b4b60188a18a2a6b588dc4b2d">00587</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="l00589"></a><a class="code" href="classbdm_1_1mgamma__fix.html#1bfd30e90db9dc1fbda4a9fbb0b716b2">00589</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="l00590"></a>00590                 <a class="code" href="classbdm_1_1mgamma.html#0b486f7e52a3d8a39adbcbd325461c0d" title="Set value of k.">mgamma::set_parameters</a> ( k0 );
-<a name="l00591"></a>00591                 <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="l00592"></a>00592         };
-<a name="l00593"></a>00593 
-<a name="l00594"></a><a class="code" href="classbdm_1_1mgamma__fix.html#1d539591deb7a38bb3403c2b396c8ff7">00594</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="l00595"></a>00595 };
-<a name="l00596"></a>00596 
-<a name="l00597"></a>00597 
-<a name="l00610"></a><a class="code" href="classbdm_1_1migamma__fix.html">00610</a> <span class="keyword">class </span><a class="code" href="classbdm_1_1migamma__fix.html" title="Inverse-Gamma random walk around a fixed point.">migamma_fix</a> : <span class="keyword">public</span> <a class="code" href="classbdm_1_1migamma.html" title="Inverse-Gamma random walk.">migamma</a> {
-<a name="l00611"></a>00611 <span class="keyword">protected</span>:
-<a name="l00613"></a><a class="code" href="classbdm_1_1migamma__fix.html#e1c344accac36d7ccc3ffa502e8d2f4e">00613</a>         <span class="keywordtype">double</span> <a class="code" href="classbdm_1_1migamma__fix.html#e1c344accac36d7ccc3ffa502e8d2f4e" title="parameter l">l</a>;
-<a name="l00615"></a><a class="code" href="classbdm_1_1migamma__fix.html#5d453e5a2bdfc9a16c8acb8842dc9780">00615</a>         vec <a class="code" href="classbdm_1_1migamma__fix.html#5d453e5a2bdfc9a16c8acb8842dc9780" title="reference vector">refl</a>;
-<a name="l00616"></a>00616 <span class="keyword">public</span>:
-<a name="l00618"></a><a class="code" href="classbdm_1_1migamma__fix.html#42a61f9468b2c435386f47ae8a5ddf7e">00618</a>         <a class="code" href="classbdm_1_1migamma__fix.html#42a61f9468b2c435386f47ae8a5ddf7e" title="Constructor.">migamma_fix</a> ( ) : <a class="code" href="classbdm_1_1migamma.html" title="Inverse-Gamma random walk.">migamma</a> (),<a class="code" href="classbdm_1_1migamma__fix.html#5d453e5a2bdfc9a16c8acb8842dc9780" title="reference vector">refl</a> ( ) {};
-<a name="l00620"></a><a class="code" href="classbdm_1_1migamma__fix.html#17f9ce1068660a4e8c05173bef7d7440">00620</a>         <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1migamma__fix.html#17f9ce1068660a4e8c05173bef7d7440" title="Set value of k.">set_parameters</a> ( <span class="keywordtype">double</span> k0 , vec ref0, <span class="keywordtype">double</span> l0 ) {
-<a name="l00621"></a>00621                 <a class="code" href="classbdm_1_1migamma.html#8b10ab922e2a7bae2fb6bb3efc7b6151" title="Set value of k.">migamma::set_parameters</a> (ref0.length(), k0 );
-<a name="l00622"></a>00622                 <a class="code" href="classbdm_1_1migamma__fix.html#5d453e5a2bdfc9a16c8acb8842dc9780" title="reference vector">refl</a>=pow ( ref0,1.0-l0 );
-<a name="l00623"></a>00623                 <a class="code" href="classbdm_1_1migamma__fix.html#e1c344accac36d7ccc3ffa502e8d2f4e" title="parameter l">l</a>=l0;
-<a name="l00624"></a>00624         };
-<a name="l00625"></a>00625 
-<a name="l00626"></a><a class="code" href="classbdm_1_1migamma__fix.html#cfbabd293795d44aae1b7c874e57c4b8">00626</a>         <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1migamma__fix.html#cfbabd293795d44aae1b7c874e57c4b8" 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="l00627"></a>00627                 vec mean=elem_mult ( <a class="code" href="classbdm_1_1migamma__fix.html#5d453e5a2bdfc9a16c8acb8842dc9780" title="reference vector">refl</a>,pow ( val,<a class="code" href="classbdm_1_1migamma__fix.html#e1c344accac36d7ccc3ffa502e8d2f4e" title="parameter l">l</a> ) );
-<a name="l00628"></a>00628                 <a class="code" href="classbdm_1_1migamma.html#7a34b1e2e3aa2250d7c0ed7df1665b8c" title="Update ep so that it represents this mpdf conditioned on rvc = cond.">migamma::condition</a> ( mean );
-<a name="l00629"></a>00629         };
-<a name="l00630"></a>00630 };
-<a name="l00632"></a><a class="code" href="namespacebdm.html#33aac0be76ded31d2e3081c5a3f6c418">00632</a> <span class="keyword">enum</span> <a class="code" href="namespacebdm.html#33aac0be76ded31d2e3081c5a3f6c418" title="Switch between various resampling methods.">RESAMPLING_METHOD</a> { MULTINOMIAL = 0, STRATIFIED = 1, SYSTEMATIC = 3 };
-<a name="l00638"></a><a class="code" href="classbdm_1_1eEmp.html">00638</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="l00639"></a>00639 <span class="keyword">protected</span> :
-<a name="l00641"></a><a class="code" href="classbdm_1_1eEmp.html#9798006271ca77629855113f1283a031">00641</a>         <span class="keywordtype">int</span> <a class="code" href="classbdm_1_1eEmp.html#9798006271ca77629855113f1283a031" title="Number of particles.">n</a>;
-<a name="l00643"></a><a class="code" href="classbdm_1_1eEmp.html#9d39241aab7c4bbeb07c6d516421c67d">00643</a>         vec <a class="code" href="classbdm_1_1eEmp.html#9d39241aab7c4bbeb07c6d516421c67d" title="Sample weights .">w</a>;
-<a name="l00645"></a><a class="code" href="classbdm_1_1eEmp.html#73d819553a0f268b055a087d2d4486f3">00645</a>         Array&lt;vec&gt; <a class="code" href="classbdm_1_1eEmp.html#73d819553a0f268b055a087d2d4486f3" title="Samples .">samples</a>;
-<a name="l00646"></a>00646 <span class="keyword">public</span>:
-<a name="l00648"></a><a class="code" href="classbdm_1_1eEmp.html#34d7f929fbab6c1e4ce318c56a6399c7">00648</a>         <a class="code" href="classbdm_1_1eEmp.html#34d7f929fbab6c1e4ce318c56a6399c7" title="Default constructor.">eEmp</a> ( <span class="keywordtype">int</span> n0 ) :<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#9798006271ca77629855113f1283a031" title="Number of particles.">n</a> ( n0 ),<a class="code" href="classbdm_1_1eEmp.html#9d39241aab7c4bbeb07c6d516421c67d" title="Sample weights .">w</a> ( <a class="code" href="classbdm_1_1eEmp.html#9798006271ca77629855113f1283a031" title="Number of particles.">n</a> ),<a class="code" href="classbdm_1_1eEmp.html#73d819553a0f268b055a087d2d4486f3" title="Samples .">samples</a> ( <a class="code" href="classbdm_1_1eEmp.html#9798006271ca77629855113f1283a031" title="Number of particles.">n</a> ) {};
-<a name="l00650"></a>00650         <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1eEmp.html#82320074a9b0ad7e1bb33a6e885b65d7" title="Set samples and weights.">set_parameters</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="l00652"></a>00652         <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="l00654"></a><a class="code" href="classbdm_1_1eEmp.html#dccd02eaa4c45e858a6351723686ac85">00654</a>         <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1eEmp.html#dccd02eaa4c45e858a6351723686ac85" title="Set sample.">set_n</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#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="l00656"></a><a class="code" href="classbdm_1_1eEmp.html#d7f83cc0415cd44ae7cc8b4bdad93aef">00656</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="l00658"></a><a class="code" href="classbdm_1_1eEmp.html#b7d7106f486e3fad38590914a693d714">00658</a>         <span class="keyword">const</span> vec&amp; <a class="code" href="classbdm_1_1eEmp.html#d7f83cc0415cd44ae7cc8b4bdad93aef" 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="l00660"></a><a class="code" href="classbdm_1_1eEmp.html#c24966b0aaeb767bc8a6b4fd60931be2">00660</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="l00662"></a><a class="code" href="classbdm_1_1eEmp.html#b59af0efdb009d98ea8ebfa965e74ae2">00662</a>         <span class="keyword">const</span> Array&lt;vec&gt;&amp; <a class="code" href="classbdm_1_1eEmp.html#c24966b0aaeb767bc8a6b4fd60931be2" 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="l00664"></a>00664         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> ( <a class="code" href="namespacebdm.html#33aac0be76ded31d2e3081c5a3f6c418" title="Switch between various resampling methods.">RESAMPLING_METHOD</a> method = SYSTEMATIC );
-<a name="l00666"></a><a class="code" href="classbdm_1_1eEmp.html#97f1e07b5ae6eebc91c7365f0f88d270">00666</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="l00668"></a><a class="code" href="classbdm_1_1eEmp.html#01654c014d3aa068f8d4ecba4be86d09">00668</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="l00669"></a><a class="code" href="classbdm_1_1eEmp.html#bbfcb4f868c7381298c281a256d8c4b9">00669</a>         vec <a class="code" href="classbdm_1_1eEmp.html#bbfcb4f868c7381298c281a256d8c4b9" title="return expected value">mean</a>()<span class="keyword"> const </span>{
-<a name="l00670"></a>00670                 vec pom=zeros ( <a class="code" href="classbdm_1_1epdf.html#16adac20ec7fe07e1ea0b27d917788ce" title="dimension of the random variable">dim</a> );
-<a name="l00671"></a>00671                 <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="l00672"></a>00672                 <span class="keywordflow">return</span> pom;
-<a name="l00673"></a>00673         }
-<a name="l00674"></a><a class="code" href="classbdm_1_1eEmp.html#05e9ebf467ede737cb6a3621d7fd3c87">00674</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="l00675"></a>00675                 vec pom=zeros ( <a class="code" href="classbdm_1_1epdf.html#16adac20ec7fe07e1ea0b27d917788ce" title="dimension of the random variable">dim</a> );
-<a name="l00676"></a>00676                 <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="l00677"></a>00677                 <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="l00678"></a>00678         }
-<a name="l00679"></a>00679 };
-<a name="l00680"></a>00680 
-<a name="l00681"></a>00681 
-<a name="l00683"></a>00683 
-<a name="l00684"></a>00684 <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt;
-<a name="l00685"></a>00685 enorm&lt;sq_T&gt;::enorm ( ) :eEF ( ), mu ( ),R ( ) {};
-<a name="l00686"></a>00686 
-<a name="l00687"></a>00687 <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt;
-<a name="l00688"></a>00688 <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="l00689"></a>00689 <span class="comment">//Fixme test dimensions of mu0 and R0;</span>
-<a name="l00690"></a>00690         mu = mu0;
-<a name="l00691"></a>00691         R = R0;
-<a name="l00692"></a>00692         <a class="code" href="classbdm_1_1epdf.html#16adac20ec7fe07e1ea0b27d917788ce" title="dimension of the random variable">dim</a> = mu0.length();
-<a name="l00693"></a>00693 };
-<a name="l00694"></a>00694 
-<a name="l00695"></a>00695 <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt;
-<a name="l00696"></a><a class="code" href="classbdm_1_1enorm.html#d2e0d3a1e30ab3ab04df2d0c43ae74a2">00696</a> <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1enorm.html" title="Gaussian density with positive definite (decomposed) covariance matrix.">enorm&lt;sq_T&gt;::dupdate</a> ( mat &amp;v, <span class="keywordtype">double</span> nu ) {
-<a name="l00697"></a>00697         <span class="comment">//</span>
-<a name="l00698"></a>00698 };
-<a name="l00699"></a>00699 
-<a name="l00700"></a>00700 <span class="comment">// template&lt;class sq_T&gt;</span>
-<a name="l00701"></a>00701 <span class="comment">// void enorm&lt;sq_T&gt;::tupdate ( double phi, mat &amp;vbar, double nubar ) {</span>
-<a name="l00702"></a>00702 <span class="comment">//      //</span>
-<a name="l00703"></a>00703 <span class="comment">// };</span>
-<a name="l00704"></a>00704 
-<a name="l00705"></a>00705 <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt;
-<a name="l00706"></a><a class="code" href="classbdm_1_1enorm.html#e1a48f52351ec3a349bd443b713b1766">00706</a> vec <a class="code" href="classbdm_1_1enorm.html" title="Gaussian density with positive definite (decomposed) covariance matrix.">enorm&lt;sq_T&gt;::sample</a>()<span class="keyword"> const </span>{
-<a name="l00707"></a>00707         vec x ( <a class="code" href="classbdm_1_1epdf.html#16adac20ec7fe07e1ea0b27d917788ce" title="dimension of the random variable">dim</a> );
-<a name="l00708"></a>00708 <span class="preprocessor">#pragma omp critical</span>
-<a name="l00709"></a>00709 <span class="preprocessor"></span>        <a class="code" href="namespacebdm.html#c959a7382efbcc31af4b58cf0f0f951a" title="Global Normal_RNG.">NorRNG</a>.sample_vector ( <a class="code" href="classbdm_1_1epdf.html#16adac20ec7fe07e1ea0b27d917788ce" title="dimension of the random variable">dim</a>,x );
-<a name="l00710"></a>00710         vec smp = <a class="code" href="classbdm_1_1enorm.html#2d92dde696b2a7a5b10ddef5d22ba2c2" title="Covariance matrix in decomposed form.">R</a>.sqrt_mult ( x );
-<a name="l00711"></a>00711 
-<a name="l00712"></a>00712         smp += <a class="code" href="classbdm_1_1enorm.html#c702a194720853570d08b65482f842c7" title="mean value">mu</a>;
-<a name="l00713"></a>00713         <span class="keywordflow">return</span> smp;
-<a name="l00714"></a>00714 };
-<a name="l00715"></a>00715 
-<a name="l00716"></a>00716 <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt;
-<a name="l00717"></a>00717 mat <a class="code" href="classbdm_1_1enorm.html" title="Gaussian density with positive definite (decomposed) covariance matrix.">enorm&lt;sq_T&gt;::sample</a> ( <span class="keywordtype">int</span> N )<span class="keyword"> const </span>{
-<a name="l00718"></a>00718         mat X ( <a class="code" href="classbdm_1_1epdf.html#16adac20ec7fe07e1ea0b27d917788ce" title="dimension of the random variable">dim</a>,N );
-<a name="l00719"></a>00719         vec x ( <a class="code" href="classbdm_1_1epdf.html#16adac20ec7fe07e1ea0b27d917788ce" title="dimension of the random variable">dim</a> );
-<a name="l00720"></a>00720         vec pom;
-<a name="l00721"></a>00721         <span class="keywordtype">int</span> i;
-<a name="l00722"></a>00722 
-<a name="l00723"></a>00723         <span class="keywordflow">for</span> ( i=0;i&lt;N;i++ ) {
-<a name="l00724"></a>00724 <span class="preprocessor">#pragma omp critical</span>
-<a name="l00725"></a>00725 <span class="preprocessor"></span>                <a class="code" href="namespacebdm.html#c959a7382efbcc31af4b58cf0f0f951a" title="Global Normal_RNG.">NorRNG</a>.sample_vector ( <a class="code" href="classbdm_1_1epdf.html#16adac20ec7fe07e1ea0b27d917788ce" title="dimension of the random variable">dim</a>,x );
-<a name="l00726"></a>00726                 pom = <a class="code" href="classbdm_1_1enorm.html#2d92dde696b2a7a5b10ddef5d22ba2c2" title="Covariance matrix in decomposed form.">R</a>.sqrt_mult ( x );
-<a name="l00727"></a>00727                 pom +=<a class="code" href="classbdm_1_1enorm.html#c702a194720853570d08b65482f842c7" title="mean value">mu</a>;
-<a name="l00728"></a>00728                 X.set_col ( i, pom );
-<a name="l00729"></a>00729         }
+<a name="l00429"></a>00429         <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="l00430"></a><a class="code" href="classbdm_1_1euni.html#caa07b8307bd793d5339d6583e0aba81">00430</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="l00431"></a><a class="code" href="classbdm_1_1euni.html#fc5df80359ead2918384b2004ce67194">00431</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="l00432"></a>00432                 vec smp ( <a class="code" href="classbdm_1_1epdf.html#16adac20ec7fe07e1ea0b27d917788ce" title="dimension of the random variable">dim</a> );
+<a name="l00433"></a>00433 <span class="preprocessor">#pragma omp critical</span>
+<a name="l00434"></a>00434 <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="l00435"></a>00435                 <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="l00436"></a>00436         }
+<a name="l00438"></a><a class="code" href="classbdm_1_1euni.html#46caa8c13aba2e6228f964208918b226">00438</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="l00439"></a><a class="code" href="classbdm_1_1euni.html#951f932155111f6053c980f672b4c22c">00439</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="l00440"></a>00440 };
+<a name="l00441"></a>00441 
+<a name="l00442"></a>00442 
+<a name="l00448"></a>00448 <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt;
+<a name="l00449"></a><a class="code" href="classbdm_1_1mlnorm.html">00449</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="l00450"></a>00450 <span class="keyword">protected</span>:
+<a name="l00452"></a><a class="code" href="classbdm_1_1mlnorm.html#150ad6acb223b0a0abeaf92346686dcd">00452</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="l00453"></a>00453         mat A;
+<a name="l00454"></a>00454         vec mu_const;
+<a name="l00455"></a>00455         vec&amp; _mu; <span class="comment">//cached epdf.mu;</span>
+<a name="l00456"></a>00456 <span class="keyword">public</span>:
+<a name="l00459"></a>00459         <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="l00460"></a>00460         <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="l00461"></a>00461                 <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="l00462"></a>00462         };
+<a name="l00464"></a>00464         <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="l00467"></a>00467         <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="l00468"></a>00468 
+<a name="l00470"></a><a class="code" href="classbdm_1_1mlnorm.html#56e587952f94fcac6cfc999eae6dbced">00470</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="l00472"></a><a class="code" href="classbdm_1_1mlnorm.html#262a2a486bff585f34bb6a5005b02614">00472</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="l00474"></a><a class="code" href="classbdm_1_1mlnorm.html#78120ecd1c2b1d7e80124b4603504604">00474</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="l00475"></a>00475 
+<a name="l00476"></a>00476         <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_M&gt;
+<a name="l00477"></a>00477         <span class="keyword">friend</span> std::ostream &amp;operator&lt;&lt; ( std::ostream &amp;os,  mlnorm&lt;sq_M&gt; &amp;ml );
+<a name="l00478"></a>00478 };
+<a name="l00479"></a>00479 
+<a name="l00487"></a><a class="code" href="classbdm_1_1mlstudent.html">00487</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="l00488"></a>00488 <span class="keyword">protected</span>:
+<a name="l00489"></a>00489         <a class="code" href="classldmat.html" title="Matrix stored in LD form, (commonly known as UD).">ldmat</a> Lambda;
+<a name="l00490"></a>00490         <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="l00491"></a>00491         <a class="code" href="classldmat.html" title="Matrix stored in LD form, (commonly known as UD).">ldmat</a> Re;
+<a name="l00492"></a>00492 <span class="keyword">public</span>:
+<a name="l00493"></a>00493         <a class="code" href="classbdm_1_1mlstudent.html">mlstudent</a> ( ) :<a class="code" href="classbdm_1_1mlnorm.html" title="Normal distributed linear function with linear function of mean value;.">mlnorm&lt;ldmat&gt;</a> (),
+<a name="l00494"></a>00494                         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="l00495"></a>00495         <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="l00496"></a>00496                 it_assert_debug ( A0.rows() ==mu0.length(),<span class="stringliteral">""</span> );
+<a name="l00497"></a>00497                 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="l00498"></a>00498 
+<a name="l00499"></a>00499                 <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="l00500"></a>00500                 A = A0;
+<a name="l00501"></a>00501                 mu_const = mu0;
+<a name="l00502"></a>00502                 Re=R0;
+<a name="l00503"></a>00503                 Lambda = Lambda0;
+<a name="l00504"></a>00504         }
+<a name="l00505"></a><a class="code" href="classbdm_1_1mlstudent.html#efd37560585c8613897f30d3c2f58d0d">00505</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="l00506"></a>00506                 _mu = A*cond + mu_const;
+<a name="l00507"></a>00507                 <span class="keywordtype">double</span> zeta;
+<a name="l00508"></a>00508                 <span class="comment">//ugly hack!</span>
+<a name="l00509"></a>00509                 <span class="keywordflow">if</span> ( ( cond.length() +1 ) ==Lambda.<a class="code" href="group__math.html#g96dfb21865db4f5bd36fa70f9b0b1163" title="access function">rows</a>() ) {
+<a name="l00510"></a>00510                         zeta = Lambda.<a class="code" href="classldmat.html#d876c5f83e02b3e809b35c9de5068f14" title="Evaluates quadratic form ;.">invqform</a> ( concat ( cond, vec_1 ( 1.0 ) ) );
+<a name="l00511"></a>00511                 }
+<a name="l00512"></a>00512                 <span class="keywordflow">else</span> {
+<a name="l00513"></a>00513                         zeta = Lambda.<a class="code" href="classldmat.html#d876c5f83e02b3e809b35c9de5068f14" title="Evaluates quadratic form ;.">invqform</a> ( cond );
+<a name="l00514"></a>00514                 }
+<a name="l00515"></a>00515                 <a class="code" href="classbdm_1_1mlnorm.html#78120ecd1c2b1d7e80124b4603504604" title="access function">_R</a> = Re;
+<a name="l00516"></a>00516                 <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="l00517"></a>00517         };
+<a name="l00518"></a>00518 
+<a name="l00519"></a>00519 };
+<a name="l00529"></a><a class="code" href="classbdm_1_1mgamma.html">00529</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="l00530"></a>00530 <span class="keyword">protected</span>:
+<a name="l00532"></a><a class="code" href="classbdm_1_1mgamma.html#bdc9f1e9e03c09e91103fee269864438">00532</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="l00534"></a><a class="code" href="classbdm_1_1mgamma.html#b20cf88cca1fe9b0b8f2a412608bfd09">00534</a>         <span class="keywordtype">double</span> <a class="code" href="classbdm_1_1mgamma.html#b20cf88cca1fe9b0b8f2a412608bfd09" title="Constant .">k</a>;
+<a name="l00536"></a><a class="code" href="classbdm_1_1mgamma.html#3d95f4dde9214ff6dba265e18af60312">00536</a>         vec &amp;<a class="code" href="classbdm_1_1mgamma.html#3d95f4dde9214ff6dba265e18af60312" title="cache of epdf.beta">_beta</a>;
+<a name="l00537"></a>00537 
+<a name="l00538"></a>00538 <span class="keyword">public</span>:
+<a name="l00540"></a><a class="code" href="classbdm_1_1mgamma.html#1a9dc8661e5b214a8185d6e6b9956eb1">00540</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="l00542"></a>00542         <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="l00543"></a><a class="code" href="classbdm_1_1mgamma.html#8996500f1885e39cde30221b20900bff">00543</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="l00544"></a>00544 };
+<a name="l00545"></a>00545 
+<a name="l00555"></a><a class="code" href="classbdm_1_1migamma.html">00555</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="l00556"></a>00556 <span class="keyword">protected</span>:
+<a name="l00558"></a><a class="code" href="classbdm_1_1migamma.html#a31b39d4179551b593c9e0d7d756783a">00558</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="l00560"></a><a class="code" href="classbdm_1_1migamma.html#dc56bc9da542e0103ec16b9be8e5e38c">00560</a>         <span class="keywordtype">double</span> <a class="code" href="classbdm_1_1migamma.html#dc56bc9da542e0103ec16b9be8e5e38c" title="Constant .">k</a>;
+<a name="l00562"></a><a class="code" href="classbdm_1_1migamma.html#c9847093da59a9ba0ebb68d2c592f5dc">00562</a>         vec &amp;<a class="code" href="classbdm_1_1migamma.html#c9847093da59a9ba0ebb68d2c592f5dc" title="cache of epdf.alpha">_alpha</a>;
+<a name="l00564"></a><a class="code" href="classbdm_1_1migamma.html#0d854c047001b5465cf1ba21f52904b5">00564</a>         vec &amp;<a class="code" href="classbdm_1_1migamma.html#0d854c047001b5465cf1ba21f52904b5" title="cache of epdf.beta">_beta</a>;
+<a name="l00565"></a>00565 
+<a name="l00566"></a>00566 <span class="keyword">public</span>:
+<a name="l00569"></a>00569         <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="l00570"></a>00570         <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="l00572"></a>00572 
+<a name="l00574"></a><a class="code" href="classbdm_1_1migamma.html#8b10ab922e2a7bae2fb6bb3efc7b6151">00574</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="l00575"></a>00575                 <a class="code" href="classbdm_1_1migamma.html#dc56bc9da542e0103ec16b9be8e5e38c" title="Constant .">k</a>=k0;
+<a name="l00576"></a>00576                 <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="l00577"></a>00577                 <a class="code" href="classbdm_1_1mpdf.html#7c1900976ff13dbc09c9729b3bbff9e6" title="dimension of the condition">dimc</a> = dimension();
+<a name="l00578"></a>00578         };
+<a name="l00579"></a><a class="code" href="classbdm_1_1migamma.html#7a34b1e2e3aa2250d7c0ed7df1665b8c">00579</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="l00580"></a>00580                 <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="l00581"></a>00581         };
+<a name="l00582"></a>00582 };
+<a name="l00583"></a>00583 
+<a name="l00595"></a><a class="code" href="classbdm_1_1mgamma__fix.html">00595</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="l00596"></a>00596 <span class="keyword">protected</span>:
+<a name="l00598"></a><a class="code" href="classbdm_1_1mgamma__fix.html#1eb701506aabb2e6af007e487212d6fa">00598</a>         <span class="keywordtype">double</span> <a class="code" href="classbdm_1_1mgamma__fix.html#1eb701506aabb2e6af007e487212d6fa" title="parameter l">l</a>;
+<a name="l00600"></a><a class="code" href="classbdm_1_1mgamma__fix.html#018c6f901a04e419455308a07eb3b0b2">00600</a>         vec <a class="code" href="classbdm_1_1mgamma__fix.html#018c6f901a04e419455308a07eb3b0b2" title="reference vector">refl</a>;
+<a name="l00601"></a>00601 <span class="keyword">public</span>:
+<a name="l00603"></a><a class="code" href="classbdm_1_1mgamma__fix.html#9a31bc9b4b60188a18a2a6b588dc4b2d">00603</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="l00605"></a><a class="code" href="classbdm_1_1mgamma__fix.html#1bfd30e90db9dc1fbda4a9fbb0b716b2">00605</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="l00606"></a>00606                 <a class="code" href="classbdm_1_1mgamma.html#a0f21c2557b233a85838b497d040ab14" title="Set value of k.">mgamma::set_parameters</a> ( k0, ref0 );
+<a name="l00607"></a>00607                 <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="l00608"></a>00608                 <a class="code" href="classbdm_1_1mpdf.html#7c1900976ff13dbc09c9729b3bbff9e6" title="dimension of the condition">dimc</a>=dimension();
+<a name="l00609"></a>00609         };
+<a name="l00610"></a>00610 
+<a name="l00611"></a><a class="code" href="classbdm_1_1mgamma__fix.html#1d539591deb7a38bb3403c2b396c8ff7">00611</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="l00612"></a>00612 };
+<a name="l00613"></a>00613 
+<a name="l00614"></a>00614 
+<a name="l00627"></a><a class="code" href="classbdm_1_1migamma__fix.html">00627</a> <span class="keyword">class </span><a class="code" href="classbdm_1_1migamma__fix.html" title="Inverse-Gamma random walk around a fixed point.">migamma_fix</a> : <span class="keyword">public</span> <a class="code" href="classbdm_1_1migamma.html" title="Inverse-Gamma random walk.">migamma</a> {
+<a name="l00628"></a>00628 <span class="keyword">protected</span>:
+<a name="l00630"></a><a class="code" href="classbdm_1_1migamma__fix.html#e1c344accac36d7ccc3ffa502e8d2f4e">00630</a>         <span class="keywordtype">double</span> <a class="code" href="classbdm_1_1migamma__fix.html#e1c344accac36d7ccc3ffa502e8d2f4e" title="parameter l">l</a>;
+<a name="l00632"></a><a class="code" href="classbdm_1_1migamma__fix.html#5d453e5a2bdfc9a16c8acb8842dc9780">00632</a>         vec <a class="code" href="classbdm_1_1migamma__fix.html#5d453e5a2bdfc9a16c8acb8842dc9780" title="reference vector">refl</a>;
+<a name="l00633"></a>00633 <span class="keyword">public</span>:
+<a name="l00635"></a><a class="code" href="classbdm_1_1migamma__fix.html#42a61f9468b2c435386f47ae8a5ddf7e">00635</a>         <a class="code" href="classbdm_1_1migamma__fix.html#42a61f9468b2c435386f47ae8a5ddf7e" title="Constructor.">migamma_fix</a> ( ) : <a class="code" href="classbdm_1_1migamma.html" title="Inverse-Gamma random walk.">migamma</a> (),<a class="code" href="classbdm_1_1migamma__fix.html#5d453e5a2bdfc9a16c8acb8842dc9780" title="reference vector">refl</a> ( ) {};
+<a name="l00637"></a><a class="code" href="classbdm_1_1migamma__fix.html#17f9ce1068660a4e8c05173bef7d7440">00637</a>         <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1migamma__fix.html#17f9ce1068660a4e8c05173bef7d7440" title="Set value of k.">set_parameters</a> ( <span class="keywordtype">double</span> k0 , vec ref0, <span class="keywordtype">double</span> l0 ) {
+<a name="l00638"></a>00638                 <a class="code" href="classbdm_1_1migamma.html#8b10ab922e2a7bae2fb6bb3efc7b6151" title="Set value of k.">migamma::set_parameters</a> ( ref0.length(), k0 );
+<a name="l00639"></a>00639                 <a class="code" href="classbdm_1_1migamma__fix.html#5d453e5a2bdfc9a16c8acb8842dc9780" title="reference vector">refl</a>=pow ( ref0,1.0-l0 );
+<a name="l00640"></a>00640                 <a class="code" href="classbdm_1_1migamma__fix.html#e1c344accac36d7ccc3ffa502e8d2f4e" title="parameter l">l</a>=l0;
+<a name="l00641"></a>00641                 <a class="code" href="classbdm_1_1mpdf.html#7c1900976ff13dbc09c9729b3bbff9e6" title="dimension of the condition">dimc</a> = dimension();
+<a name="l00642"></a>00642         };
+<a name="l00643"></a>00643 
+<a name="l00644"></a><a class="code" href="classbdm_1_1migamma__fix.html#cfbabd293795d44aae1b7c874e57c4b8">00644</a>         <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1migamma__fix.html#cfbabd293795d44aae1b7c874e57c4b8" 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="l00645"></a>00645                 vec mean=elem_mult ( <a class="code" href="classbdm_1_1migamma__fix.html#5d453e5a2bdfc9a16c8acb8842dc9780" title="reference vector">refl</a>,pow ( val,<a class="code" href="classbdm_1_1migamma__fix.html#e1c344accac36d7ccc3ffa502e8d2f4e" title="parameter l">l</a> ) );
+<a name="l00646"></a>00646                 <a class="code" href="classbdm_1_1migamma.html#7a34b1e2e3aa2250d7c0ed7df1665b8c" title="Update ep so that it represents this mpdf conditioned on rvc = cond.">migamma::condition</a> ( mean );
+<a name="l00647"></a>00647         };
+<a name="l00648"></a>00648 };
+<a name="l00650"></a>00650 <span class="keyword">enum</span> RESAMPLING_METHOD { MULTINOMIAL = 0, STRATIFIED = 1, SYSTEMATIC = 3 };
+<a name="l00656"></a><a class="code" href="classbdm_1_1eEmp.html">00656</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="l00657"></a>00657 <span class="keyword">protected</span> :
+<a name="l00659"></a><a class="code" href="classbdm_1_1eEmp.html#9798006271ca77629855113f1283a031">00659</a>         <span class="keywordtype">int</span> <a class="code" href="classbdm_1_1eEmp.html#9798006271ca77629855113f1283a031" title="Number of particles.">n</a>;
+<a name="l00661"></a><a class="code" href="classbdm_1_1eEmp.html#9d39241aab7c4bbeb07c6d516421c67d">00661</a>         vec <a class="code" href="classbdm_1_1eEmp.html#9d39241aab7c4bbeb07c6d516421c67d" title="Sample weights .">w</a>;
+<a name="l00663"></a><a class="code" href="classbdm_1_1eEmp.html#73d819553a0f268b055a087d2d4486f3">00663</a>         Array&lt;vec&gt; <a class="code" href="classbdm_1_1eEmp.html#73d819553a0f268b055a087d2d4486f3" title="Samples .">samples</a>;
+<a name="l00664"></a>00664 <span class="keyword">public</span>:
+<a name="l00667"></a>00667         <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="l00668"></a>00668         <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="l00670"></a>00670         
+<a name="l00672"></a>00672         <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1eEmp.html#82320074a9b0ad7e1bb33a6e885b65d7" title="Set samples and weights.">set_parameters</a> ( <span class="keyword">const</span> vec &amp;w0, <span class="keyword">const</span> epdf* pdf0 );
+<a name="l00674"></a>00674         <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> epdf* pdf0 );
+<a name="l00676"></a><a class="code" href="classbdm_1_1eEmp.html#dccd02eaa4c45e858a6351723686ac85">00676</a>         <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1eEmp.html#dccd02eaa4c45e858a6351723686ac85" title="Set sample.">set_n</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#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="l00678"></a><a class="code" href="classbdm_1_1eEmp.html#d7f83cc0415cd44ae7cc8b4bdad93aef">00678</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="l00680"></a><a class="code" href="classbdm_1_1eEmp.html#b7d7106f486e3fad38590914a693d714">00680</a>         <span class="keyword">const</span> vec&amp; <a class="code" href="classbdm_1_1eEmp.html#d7f83cc0415cd44ae7cc8b4bdad93aef" 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="l00682"></a><a class="code" href="classbdm_1_1eEmp.html#c24966b0aaeb767bc8a6b4fd60931be2">00682</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="l00684"></a><a class="code" href="classbdm_1_1eEmp.html#b59af0efdb009d98ea8ebfa965e74ae2">00684</a>         <span class="keyword">const</span> Array&lt;vec&gt;&amp; <a class="code" href="classbdm_1_1eEmp.html#c24966b0aaeb767bc8a6b4fd60931be2" 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="l00686"></a>00686         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="l00688"></a><a class="code" href="classbdm_1_1eEmp.html#97f1e07b5ae6eebc91c7365f0f88d270">00688</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="l00690"></a><a class="code" href="classbdm_1_1eEmp.html#01654c014d3aa068f8d4ecba4be86d09">00690</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="l00691"></a><a class="code" href="classbdm_1_1eEmp.html#bbfcb4f868c7381298c281a256d8c4b9">00691</a>         vec <a class="code" href="classbdm_1_1eEmp.html#bbfcb4f868c7381298c281a256d8c4b9" title="return expected value">mean</a>()<span class="keyword"> const </span>{
+<a name="l00692"></a>00692                 vec pom=zeros ( <a class="code" href="classbdm_1_1epdf.html#16adac20ec7fe07e1ea0b27d917788ce" title="dimension of the random variable">dim</a> );
+<a name="l00693"></a>00693                 <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="l00694"></a>00694                 <span class="keywordflow">return</span> pom;
+<a name="l00695"></a>00695         }
+<a name="l00696"></a><a class="code" href="classbdm_1_1eEmp.html#05e9ebf467ede737cb6a3621d7fd3c87">00696</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="l00697"></a>00697                 vec pom=zeros ( <a class="code" href="classbdm_1_1epdf.html#16adac20ec7fe07e1ea0b27d917788ce" title="dimension of the random variable">dim</a> );
+<a name="l00698"></a>00698                 <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="l00699"></a>00699                 <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="l00700"></a>00700         }
+<a name="l00701"></a>00701 };
+<a name="l00702"></a>00702 
+<a name="l00703"></a>00703 
+<a name="l00705"></a>00705 
+<a name="l00706"></a>00706 <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt;
+<a name="l00707"></a>00707 <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="l00708"></a>00708 <span class="comment">//Fixme test dimensions of mu0 and R0;</span>
+<a name="l00709"></a>00709         mu = mu0;
+<a name="l00710"></a>00710         R = R0;
+<a name="l00711"></a>00711         <a class="code" href="classbdm_1_1epdf.html#16adac20ec7fe07e1ea0b27d917788ce" title="dimension of the random variable">dim</a> = mu0.length();
+<a name="l00712"></a>00712 };
+<a name="l00713"></a>00713 
+<a name="l00714"></a>00714 <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt;
+<a name="l00715"></a><a class="code" href="classbdm_1_1enorm.html#d2e0d3a1e30ab3ab04df2d0c43ae74a2">00715</a> <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1enorm.html" title="Gaussian density with positive definite (decomposed) covariance matrix.">enorm&lt;sq_T&gt;::dupdate</a> ( mat &amp;v, <span class="keywordtype">double</span> nu ) {
+<a name="l00716"></a>00716         <span class="comment">//</span>
+<a name="l00717"></a>00717 };
+<a name="l00718"></a>00718 
+<a name="l00719"></a>00719 <span class="comment">// template&lt;class sq_T&gt;</span>
+<a name="l00720"></a>00720 <span class="comment">// void enorm&lt;sq_T&gt;::tupdate ( double phi, mat &amp;vbar, double nubar ) {</span>
+<a name="l00721"></a>00721 <span class="comment">//      //</span>
+<a name="l00722"></a>00722 <span class="comment">// };</span>
+<a name="l00723"></a>00723 
+<a name="l00724"></a>00724 <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt;
+<a name="l00725"></a><a class="code" href="classbdm_1_1enorm.html#e1a48f52351ec3a349bd443b713b1766">00725</a> vec <a class="code" href="classbdm_1_1enorm.html" title="Gaussian density with positive definite (decomposed) covariance matrix.">enorm&lt;sq_T&gt;::sample</a>()<span class="keyword"> const </span>{
+<a name="l00726"></a>00726         vec x ( <a class="code" href="classbdm_1_1epdf.html#16adac20ec7fe07e1ea0b27d917788ce" title="dimension of the random variable">dim</a> );
+<a name="l00727"></a>00727 <span class="preprocessor">#pragma omp critical</span>
+<a name="l00728"></a>00728 <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="l00729"></a>00729         vec smp = <a class="code" href="classbdm_1_1enorm.html#2d92dde696b2a7a5b10ddef5d22ba2c2" title="Covariance matrix in decomposed form.">R</a>.sqrt_mult ( x );
 <a name="l00730"></a>00730 
-<a name="l00731"></a>00731         <span class="keywordflow">return</span> X;
-<a name="l00732"></a>00732 };
-<a name="l00733"></a>00733 
-<a name="l00734"></a>00734 <span class="comment">// template&lt;class sq_T&gt;</span>
-<a name="l00735"></a>00735 <span class="comment">// double enorm&lt;sq_T&gt;::eval ( const vec &amp;val ) const {</span>
-<a name="l00736"></a>00736 <span class="comment">//      double pdfl,e;</span>
-<a name="l00737"></a>00737 <span class="comment">//      pdfl = evallog ( val );</span>
-<a name="l00738"></a>00738 <span class="comment">//      e = exp ( pdfl );</span>
-<a name="l00739"></a>00739 <span class="comment">//      return e;</span>
-<a name="l00740"></a>00740 <span class="comment">// };</span>
+<a name="l00731"></a>00731         smp += <a class="code" href="classbdm_1_1enorm.html#c702a194720853570d08b65482f842c7" title="mean value">mu</a>;
+<a name="l00732"></a>00732         <span class="keywordflow">return</span> smp;
+<a name="l00733"></a>00733 };
+<a name="l00734"></a>00734 
+<a name="l00735"></a>00735 <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt;
+<a name="l00736"></a>00736 mat <a class="code" href="classbdm_1_1enorm.html" title="Gaussian density with positive definite (decomposed) covariance matrix.">enorm&lt;sq_T&gt;::sample</a> ( <span class="keywordtype">int</span> N )<span class="keyword"> const </span>{
+<a name="l00737"></a>00737         mat X ( <a class="code" href="classbdm_1_1epdf.html#16adac20ec7fe07e1ea0b27d917788ce" title="dimension of the random variable">dim</a>,N );
+<a name="l00738"></a>00738         vec x ( <a class="code" href="classbdm_1_1epdf.html#16adac20ec7fe07e1ea0b27d917788ce" title="dimension of the random variable">dim</a> );
+<a name="l00739"></a>00739         vec pom;
+<a name="l00740"></a>00740         <span class="keywordtype">int</span> i;
 <a name="l00741"></a>00741 
-<a name="l00742"></a>00742 <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt;
-<a name="l00743"></a><a class="code" href="classbdm_1_1enorm.html#e13aeed5b543b2179bacdc4fa2ae47a3">00743</a> <span class="keywordtype">double</span> <a class="code" href="classbdm_1_1enorm.html" title="Gaussian density with positive definite (decomposed) covariance matrix.">enorm&lt;sq_T&gt;::evallog_nn</a> ( <span class="keyword">const</span> vec &amp;val )<span class="keyword"> const </span>{
-<a name="l00744"></a>00744         <span class="comment">// 1.83787706640935 = log(2pi)</span>
-<a name="l00745"></a>00745         <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="l00746"></a>00746         <span class="keywordflow">return</span>  tmp;
-<a name="l00747"></a>00747 };
-<a name="l00748"></a>00748 
-<a name="l00749"></a>00749 <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt;
-<a name="l00750"></a><a class="code" href="classbdm_1_1enorm.html#25785343aff102cc5df1cab08ba16d32">00750</a> <span class="keyword">inline</span> <span class="keywordtype">double</span> <a class="code" href="classbdm_1_1enorm.html" title="Gaussian density with positive definite (decomposed) covariance matrix.">enorm&lt;sq_T&gt;::lognc</a> ()<span class="keyword"> const </span>{
-<a name="l00751"></a>00751         <span class="comment">// 1.83787706640935 = log(2pi)</span>
-<a name="l00752"></a>00752         <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="l00753"></a>00753         <span class="keywordflow">return</span> tmp;
-<a name="l00754"></a>00754 };
-<a name="l00755"></a>00755 
-<a name="l00756"></a>00756 <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt;
-<a name="l00757"></a><a class="code" href="classbdm_1_1mlnorm.html#2dcc06a3dd71f038efbe14dc34d937ae">00757</a> <a class="code" href="classbdm_1_1mlnorm.html" title="Normal distributed linear function with linear function of mean value;.">mlnorm&lt;sq_T&gt;::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 name="l00758"></a>00758         <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="l00759"></a>00759 }
+<a name="l00742"></a>00742         <span class="keywordflow">for</span> ( i=0;i&lt;N;i++ ) {
+<a name="l00743"></a>00743 <span class="preprocessor">#pragma omp critical</span>
+<a name="l00744"></a>00744 <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="l00745"></a>00745                 pom = <a class="code" href="classbdm_1_1enorm.html#2d92dde696b2a7a5b10ddef5d22ba2c2" title="Covariance matrix in decomposed form.">R</a>.sqrt_mult ( x );
+<a name="l00746"></a>00746                 pom +=<a class="code" href="classbdm_1_1enorm.html#c702a194720853570d08b65482f842c7" title="mean value">mu</a>;
+<a name="l00747"></a>00747                 X.set_col ( i, pom );
+<a name="l00748"></a>00748         }
+<a name="l00749"></a>00749 
+<a name="l00750"></a>00750         <span class="keywordflow">return</span> X;
+<a name="l00751"></a>00751 };
+<a name="l00752"></a>00752 
+<a name="l00753"></a>00753 <span class="comment">// template&lt;class sq_T&gt;</span>
+<a name="l00754"></a>00754 <span class="comment">// double enorm&lt;sq_T&gt;::eval ( const vec &amp;val ) const {</span>
+<a name="l00755"></a>00755 <span class="comment">//      double pdfl,e;</span>
+<a name="l00756"></a>00756 <span class="comment">//      pdfl = evallog ( val );</span>
+<a name="l00757"></a>00757 <span class="comment">//      e = exp ( pdfl );</span>
+<a name="l00758"></a>00758 <span class="comment">//      return e;</span>
+<a name="l00759"></a>00759 <span class="comment">// };</span>
 <a name="l00760"></a>00760 
 <a name="l00761"></a>00761 <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt;
-<a name="l00762"></a><a class="code" href="classbdm_1_1mlnorm.html#5d18dec3167584338a4775c1d165d96f">00762</a> <span class="keywordtype">void</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;::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="l00763"></a>00763         it_assert_debug ( A0.rows() ==mu0.length(),<span class="stringliteral">""</span> );
-<a name="l00764"></a>00764         it_assert_debug ( A0.rows() ==R0.rows(),<span class="stringliteral">""</span> );
-<a name="l00765"></a>00765 
-<a name="l00766"></a>00766         <a class="code" href="classbdm_1_1epdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a>.set_parameters ( zeros ( A.rows() ),R0 );
-<a name="l00767"></a>00767         A = A0;
-<a name="l00768"></a>00768         mu_const = mu0;
-<a name="l00769"></a>00769 }
-<a name="l00770"></a>00770 
-<a name="l00771"></a>00771 <span class="comment">// template&lt;class sq_T&gt;</span>
-<a name="l00772"></a>00772 <span class="comment">// vec mlnorm&lt;sq_T&gt;::samplecond (const  vec &amp;cond, double &amp;lik ) {</span>
-<a name="l00773"></a>00773 <span class="comment">//      this-&gt;condition ( cond );</span>
-<a name="l00774"></a>00774 <span class="comment">//      vec smp = epdf.sample();</span>
-<a name="l00775"></a>00775 <span class="comment">//      lik = epdf.eval ( smp );</span>
-<a name="l00776"></a>00776 <span class="comment">//      return smp;</span>
-<a name="l00777"></a>00777 <span class="comment">// }</span>
-<a name="l00778"></a>00778 
-<a name="l00779"></a>00779 <span class="comment">// template&lt;class sq_T&gt;</span>
-<a name="l00780"></a>00780 <span class="comment">// mat mlnorm&lt;sq_T&gt;::samplecond (const vec &amp;cond, vec &amp;lik, int n ) {</span>
-<a name="l00781"></a>00781 <span class="comment">//      int i;</span>
-<a name="l00782"></a>00782 <span class="comment">//      int dim = rv.count();</span>
-<a name="l00783"></a>00783 <span class="comment">//      mat Smp ( dim,n );</span>
-<a name="l00784"></a>00784 <span class="comment">//      vec smp ( dim );</span>
-<a name="l00785"></a>00785 <span class="comment">//      this-&gt;condition ( cond );</span>
-<a name="l00786"></a>00786 <span class="comment">//</span>
-<a name="l00787"></a>00787 <span class="comment">//      for ( i=0; i&lt;n; i++ ) {</span>
-<a name="l00788"></a>00788 <span class="comment">//              smp = epdf.sample();</span>
-<a name="l00789"></a>00789 <span class="comment">//              lik ( i ) = epdf.eval ( smp );</span>
-<a name="l00790"></a>00790 <span class="comment">//              Smp.set_col ( i ,smp );</span>
-<a name="l00791"></a>00791 <span class="comment">//      }</span>
-<a name="l00792"></a>00792 <span class="comment">//</span>
-<a name="l00793"></a>00793 <span class="comment">//      return Smp;</span>
-<a name="l00794"></a>00794 <span class="comment">// }</span>
-<a name="l00795"></a>00795 
-<a name="l00796"></a>00796 <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt;
-<a name="l00797"></a><a class="code" href="classbdm_1_1mlnorm.html#0dafc0196e7e07fd06dc6716e0e18bc2">00797</a> <span class="keywordtype">void</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;::condition</a> ( <span class="keyword">const</span> vec &amp;cond ) {
-<a name="l00798"></a>00798         _mu = A*cond + mu_const;
-<a name="l00799"></a>00799 <span class="comment">//R is already assigned;</span>
-<a name="l00800"></a>00800 }
-<a name="l00801"></a>00801 
-<a name="l00802"></a>00802 <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt;
-<a name="l00803"></a><a class="code" href="classbdm_1_1enorm.html#cd02d76e9d4f96bdd3fa6b604e273039">00803</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_1enorm.html" title="Gaussian density with positive definite (decomposed) covariance matrix.">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="l00804"></a>00804         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="l00805"></a>00805 
-<a name="l00806"></a>00806         sq_T Rn ( <a class="code" href="classbdm_1_1enorm.html#2d92dde696b2a7a5b10ddef5d22ba2c2" title="Covariance matrix in decomposed form.">R</a>,irvn );
-<a name="l00807"></a>00807         <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="l00808"></a>00808         tmp-&gt;<a class="code" href="classbdm_1_1epdf.html#f423e28448dbb69ef4905295ec8de8ff" title="Name its rv.">set_rv</a> ( rvn );
-<a name="l00809"></a>00809         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="l00810"></a>00810         <span class="keywordflow">return</span> tmp;
-<a name="l00811"></a>00811 }
-<a name="l00812"></a>00812 
-<a name="l00813"></a>00813 <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt;
-<a name="l00814"></a><a class="code" href="classbdm_1_1enorm.html#baea4d49c657342b58297d68cda16d26">00814</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" title="Gaussian density with positive definite (decomposed) covariance matrix.">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="l00815"></a>00815 
-<a name="l00816"></a>00816         it_assert_debug ( <a class="code" href="classbdm_1_1epdf.html#c4b863ff84c7a4882fb3ad18556027f9" title="True if rv is assigned.">isnamed</a>(),<span class="stringliteral">""</span> );
-<a name="l00817"></a>00817 
-<a name="l00818"></a>00818         <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="l00819"></a>00819         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="l00820"></a>00820         <span class="comment">//Permutation vector of the new R</span>
-<a name="l00821"></a>00821         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="l00822"></a>00822         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="l00823"></a>00823         ivec perm=<a class="code" href="namespacebdm.html#b9016687c0e874ca5cdcf75ae28811aa" title="Concat two random variables.">concat</a> ( irvn , irvc );
-<a name="l00824"></a>00824         sq_T Rn ( <a class="code" href="classbdm_1_1enorm.html#2d92dde696b2a7a5b10ddef5d22ba2c2" title="Covariance matrix in decomposed form.">R</a>,perm );
-<a name="l00825"></a>00825 
-<a name="l00826"></a>00826         <span class="comment">//fixme - could this be done in general for all sq_T?</span>
-<a name="l00827"></a>00827         mat S=Rn.to_mat();
-<a name="l00828"></a>00828         <span class="comment">//fixme</span>
-<a name="l00829"></a>00829         <span class="keywordtype">int</span> n=rvn.<a class="code" href="classbdm_1_1RV.html#de30156104f61d86c94f758861418089">_dsize</a>()-1;
-<a name="l00830"></a>00830         <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="l00831"></a>00831         mat S11 = S.get ( 0,n, 0, n );
-<a name="l00832"></a>00832         mat S12 = S.get ( 0, n , rvn.<a class="code" href="classbdm_1_1RV.html#de30156104f61d86c94f758861418089">_dsize</a>(), end );
-<a name="l00833"></a>00833         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="l00762"></a><a class="code" href="classbdm_1_1enorm.html#e13aeed5b543b2179bacdc4fa2ae47a3">00762</a> <span class="keywordtype">double</span> <a class="code" href="classbdm_1_1enorm.html" title="Gaussian density with positive definite (decomposed) covariance matrix.">enorm&lt;sq_T&gt;::evallog_nn</a> ( <span class="keyword">const</span> vec &amp;val )<span class="keyword"> const </span>{
+<a name="l00763"></a>00763         <span class="comment">// 1.83787706640935 = log(2pi)</span>
+<a name="l00764"></a>00764         <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="l00765"></a>00765         <span class="keywordflow">return</span>  tmp;
+<a name="l00766"></a>00766 };
+<a name="l00767"></a>00767 
+<a name="l00768"></a>00768 <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt;
+<a name="l00769"></a><a class="code" href="classbdm_1_1enorm.html#25785343aff102cc5df1cab08ba16d32">00769</a> <span class="keyword">inline</span> <span class="keywordtype">double</span> <a class="code" href="classbdm_1_1enorm.html" title="Gaussian density with positive definite (decomposed) covariance matrix.">enorm&lt;sq_T&gt;::lognc</a> ()<span class="keyword"> const </span>{
+<a name="l00770"></a>00770         <span class="comment">// 1.83787706640935 = log(2pi)</span>
+<a name="l00771"></a>00771         <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="l00772"></a>00772         <span class="keywordflow">return</span> tmp;
+<a name="l00773"></a>00773 };
+<a name="l00774"></a>00774 
+<a name="l00775"></a>00775 <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt;
+<a name="l00776"></a><a class="code" href="classbdm_1_1mlnorm.html#5d18dec3167584338a4775c1d165d96f">00776</a> <span class="keywordtype">void</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;::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="l00777"></a>00777         it_assert_debug ( A0.rows() ==mu0.length(),<span class="stringliteral">""</span> );
+<a name="l00778"></a>00778         it_assert_debug ( A0.rows() ==R0.rows(),<span class="stringliteral">""</span> );
+<a name="l00779"></a>00779 
+<a name="l00780"></a>00780         <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="l00781"></a>00781         A = A0;
+<a name="l00782"></a>00782         mu_const = mu0;
+<a name="l00783"></a>00783         <a class="code" href="classbdm_1_1mpdf.html#7c1900976ff13dbc09c9729b3bbff9e6" title="dimension of the condition">dimc</a>=A0.cols();
+<a name="l00784"></a>00784 }
+<a name="l00785"></a>00785 
+<a name="l00786"></a>00786 <span class="comment">// template&lt;class sq_T&gt;</span>
+<a name="l00787"></a>00787 <span class="comment">// vec mlnorm&lt;sq_T&gt;::samplecond (const  vec &amp;cond, double &amp;lik ) {</span>
+<a name="l00788"></a>00788 <span class="comment">//      this-&gt;condition ( cond );</span>
+<a name="l00789"></a>00789 <span class="comment">//      vec smp = epdf.sample();</span>
+<a name="l00790"></a>00790 <span class="comment">//      lik = epdf.eval ( smp );</span>
+<a name="l00791"></a>00791 <span class="comment">//      return smp;</span>
+<a name="l00792"></a>00792 <span class="comment">// }</span>
+<a name="l00793"></a>00793 
+<a name="l00794"></a>00794 <span class="comment">// template&lt;class sq_T&gt;</span>
+<a name="l00795"></a>00795 <span class="comment">// mat mlnorm&lt;sq_T&gt;::samplecond (const vec &amp;cond, vec &amp;lik, int n ) {</span>
+<a name="l00796"></a>00796 <span class="comment">//      int i;</span>
+<a name="l00797"></a>00797 <span class="comment">//      int dim = rv.count();</span>
+<a name="l00798"></a>00798 <span class="comment">//      mat Smp ( dim,n );</span>
+<a name="l00799"></a>00799 <span class="comment">//      vec smp ( dim );</span>
+<a name="l00800"></a>00800 <span class="comment">//      this-&gt;condition ( cond );</span>
+<a name="l00801"></a>00801 <span class="comment">//</span>
+<a name="l00802"></a>00802 <span class="comment">//      for ( i=0; i&lt;n; i++ ) {</span>
+<a name="l00803"></a>00803 <span class="comment">//              smp = epdf.sample();</span>
+<a name="l00804"></a>00804 <span class="comment">//              lik ( i ) = epdf.eval ( smp );</span>
+<a name="l00805"></a>00805 <span class="comment">//              Smp.set_col ( i ,smp );</span>
+<a name="l00806"></a>00806 <span class="comment">//      }</span>
+<a name="l00807"></a>00807 <span class="comment">//</span>
+<a name="l00808"></a>00808 <span class="comment">//      return Smp;</span>
+<a name="l00809"></a>00809 <span class="comment">// }</span>
+<a name="l00810"></a>00810 
+<a name="l00811"></a>00811 <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt;
+<a name="l00812"></a><a class="code" href="classbdm_1_1mlnorm.html#0dafc0196e7e07fd06dc6716e0e18bc2">00812</a> <span class="keywordtype">void</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;::condition</a> ( <span class="keyword">const</span> vec &amp;cond ) {
+<a name="l00813"></a>00813         _mu = A*cond + mu_const;
+<a name="l00814"></a>00814 <span class="comment">//R is already assigned;</span>
+<a name="l00815"></a>00815 }
+<a name="l00816"></a>00816 
+<a name="l00817"></a>00817 <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt;
+<a name="l00818"></a><a class="code" href="classbdm_1_1enorm.html#cd02d76e9d4f96bdd3fa6b604e273039">00818</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_1enorm.html" title="Gaussian density with positive definite (decomposed) covariance matrix.">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="l00819"></a>00819         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="l00820"></a>00820         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="l00821"></a>00821 
+<a name="l00822"></a>00822         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="l00823"></a>00823         
+<a name="l00824"></a>00824         <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="l00825"></a>00825         tmp-&gt;<a class="code" href="classbdm_1_1epdf.html#f423e28448dbb69ef4905295ec8de8ff" title="Name its rv.">set_rv</a> ( rvn );
+<a name="l00826"></a>00826         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="l00827"></a>00827         <span class="keywordflow">return</span> tmp;
+<a name="l00828"></a>00828 }
+<a name="l00829"></a>00829 
+<a name="l00830"></a>00830 <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt;
+<a name="l00831"></a><a class="code" href="classbdm_1_1enorm.html#baea4d49c657342b58297d68cda16d26">00831</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" title="Gaussian density with positive definite (decomposed) covariance matrix.">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="l00832"></a>00832 
+<a name="l00833"></a>00833         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="l00834"></a>00834 
-<a name="l00835"></a>00835         vec mu1 = <a class="code" href="classbdm_1_1enorm.html#c702a194720853570d08b65482f842c7" title="mean value">mu</a> ( irvn );
-<a name="l00836"></a>00836         vec mu2 = <a class="code" href="classbdm_1_1enorm.html#c702a194720853570d08b65482f842c7" title="mean value">mu</a> ( irvc );
-<a name="l00837"></a>00837         mat A=S12*inv ( S22 );
-<a name="l00838"></a>00838         sq_T R_n ( S11 - A *S12.T() );
-<a name="l00839"></a>00839 
-<a name="l00840"></a>00840         <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="l00841"></a>00841 
-<a name="l00842"></a>00842         tmp-&gt;set_parameters ( A,mu1-A*mu2,R_n );
-<a name="l00843"></a>00843         <span class="keywordflow">return</span> tmp;
-<a name="l00844"></a>00844 }
-<a name="l00845"></a>00845 
-<a name="l00847"></a>00847 
-<a name="l00848"></a>00848 <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt;
-<a name="l00849"></a>00849 std::ostream &amp;operator&lt;&lt; ( std::ostream &amp;os,  mlnorm&lt;sq_T&gt; &amp;ml ) {
-<a name="l00850"></a>00850         os &lt;&lt; <span class="stringliteral">"A:"</span>&lt;&lt; ml.A&lt;&lt;endl;
-<a name="l00851"></a>00851         os &lt;&lt; <span class="stringliteral">"mu:"</span>&lt;&lt; ml.mu_const&lt;&lt;endl;
-<a name="l00852"></a>00852         os &lt;&lt; <span class="stringliteral">"R:"</span> &lt;&lt; ml.epdf._R().to_mat() &lt;&lt;endl;
-<a name="l00853"></a>00853         <span class="keywordflow">return</span> os;
-<a name="l00854"></a>00854 };
-<a name="l00855"></a>00855 
-<a name="l00856"></a>00856 }
-<a name="l00857"></a>00857 <span class="preprocessor">#endif //EF_H</span>
+<a name="l00835"></a>00835         <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="l00836"></a>00836         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="l00837"></a>00837         <span class="comment">//Permutation vector of the new R</span>
+<a name="l00838"></a>00838         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="l00839"></a>00839         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="l00840"></a>00840         ivec perm=concat ( irvn , irvc );
+<a name="l00841"></a>00841         sq_T Rn ( <a class="code" href="classbdm_1_1enorm.html#2d92dde696b2a7a5b10ddef5d22ba2c2" title="Covariance matrix in decomposed form.">R</a>,perm );
+<a name="l00842"></a>00842 
+<a name="l00843"></a>00843         <span class="comment">//fixme - could this be done in general for all sq_T?</span>
+<a name="l00844"></a>00844         mat S=Rn.to_mat();
+<a name="l00845"></a>00845         <span class="comment">//fixme</span>
+<a name="l00846"></a>00846         <span class="keywordtype">int</span> n=rvn.<a class="code" href="classbdm_1_1RV.html#de30156104f61d86c94f758861418089">_dsize</a>()-1;
+<a name="l00847"></a>00847         <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="l00848"></a>00848         mat S11 = S.get ( 0,n, 0, n );
+<a name="l00849"></a>00849         mat S12 = S.get ( 0, n , rvn.<a class="code" href="classbdm_1_1RV.html#de30156104f61d86c94f758861418089">_dsize</a>(), end );
+<a name="l00850"></a>00850         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="l00851"></a>00851 
+<a name="l00852"></a>00852         vec mu1 = <a class="code" href="classbdm_1_1enorm.html#c702a194720853570d08b65482f842c7" title="mean value">mu</a> ( irvn );
+<a name="l00853"></a>00853         vec mu2 = <a class="code" href="classbdm_1_1enorm.html#c702a194720853570d08b65482f842c7" title="mean value">mu</a> ( irvc );
+<a name="l00854"></a>00854         mat A=S12*inv ( S22 );
+<a name="l00855"></a>00855         sq_T R_n ( S11 - A *S12.T() );
+<a name="l00856"></a>00856 
+<a name="l00857"></a>00857         <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="l00858"></a>00858         tmp-&gt;set_rv(rvn); tmp-&gt;set_rvc(rvc);
+<a name="l00859"></a>00859         tmp-&gt;set_parameters ( A,mu1-A*mu2,R_n );
+<a name="l00860"></a>00860         <span class="keywordflow">return</span> tmp;
+<a name="l00861"></a>00861 }
+<a name="l00862"></a>00862 
+<a name="l00864"></a>00864 
+<a name="l00865"></a>00865 <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt;
+<a name="l00866"></a>00866 std::ostream &amp;operator&lt;&lt; ( std::ostream &amp;os,  mlnorm&lt;sq_T&gt; &amp;ml ) {
+<a name="l00867"></a>00867         os &lt;&lt; <span class="stringliteral">"A:"</span>&lt;&lt; ml.A&lt;&lt;endl;
+<a name="l00868"></a>00868         os &lt;&lt; <span class="stringliteral">"mu:"</span>&lt;&lt; ml.mu_const&lt;&lt;endl;
+<a name="l00869"></a>00869         os &lt;&lt; <span class="stringliteral">"R:"</span> &lt;&lt; ml.epdf._R().to_mat() &lt;&lt;endl;
+<a name="l00870"></a>00870         <span class="keywordflow">return</span> os;
+<a name="l00871"></a>00871 };
+<a name="l00872"></a>00872 
+<a name="l00873"></a>00873 }
+<a name="l00874"></a>00874 <span class="preprocessor">#endif //EF_H</span>
 </pre></div></div>
-<hr size="1"><address style="text-align: right;"><small>Generated on Wed Feb 11 23:33:55 2009 for mixpp by&nbsp;
+<hr size="1"><address style="text-align: right;"><small>Generated on Sun Feb 15 23:09:23 2009 for mixpp by&nbsp;
 <a href="http://www.doxygen.org/index.html">
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