Changeset 296 for doc/html/libEF_8h-source.html

Timestamp:

03/17/09 13:54:06 (16 years ago)

Author:

smidl

Message:

doc

Files:

• doc/html/libEF_8h-source.html (modified) (1 diff)

doc/html/libEF_8h-source.html

@@ -72 +72 @@
 <a name="l00019"></a>00019 <span class="comment">//#include &lt;std&gt;</span>
 <a name="l00020"></a>00020 
-<a name="l00021"></a>00021 <span class="keyword">namespace </span>bdm {
-<a name="l00022"></a>00022 
+<a name="l00021"></a>00021 <span class="keyword">namespace </span>bdm
+<a name="l00022"></a>00022 {
 <a name="l00023"></a>00023 
-<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> <a class="code" href="classitpp_1_1Gamma__RNG.html" title="Gamma distribution.">Gamma_RNG</a> 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> {
-<a name="l00038"></a>00038 <span class="keyword">public</span>:
-<a name="l00039"></a>00039 <span class="comment">//      eEF() :epdf() {};</span>
-<a name="l00041"></a><a class="code" href="classbdm_1_1eEF.html#d5459d472d0feca7cf1fb5f65c4b9ef4">00041</a> <span class="comment"></span>        <a class="code" href="classbdm_1_1eEF.html#d5459d472d0feca7cf1fb5f65c4b9ef4" title="default constructor">eEF</a> ( ) :<a class="code" href="classbdm_1_1epdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a> ( ) {};
-<a name="l00043"></a>00043         <span class="keyword">virtual</span> <span class="keywordtype">double</span> <a class="code" href="classbdm_1_1eEF.html#cd678fc9b02007a4b8d6692e746f1bf8" title="logarithm of the normalizing constant, ">lognc</a>() <span class="keyword">const</span> =0;
-<a name="l00045"></a><a class="code" href="classbdm_1_1eEF.html#deef7d6273ba4d5a5cf0bbd91ec7277a">00045</a>         <span class="keyword">virtual</span> <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1eEF.html#deef7d6273ba4d5a5cf0bbd91ec7277a" title="TODO decide if it is really needed.">dupdate</a> ( mat &amp;v ) {it_error ( <span class="stringliteral">"Not implemented"</span> );};
-<a name="l00047"></a><a class="code" href="classbdm_1_1eEF.html#a4135778ecd9ab774762936c82a097c6">00047</a>         <span class="keyword">virtual</span> <span class="keywordtype">double</span> <a class="code" href="classbdm_1_1eEF.html#a4135778ecd9ab774762936c82a097c6" title="Evaluate normalized log-probability.">evallog_nn</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="l00049"></a><a class="code" href="classbdm_1_1eEF.html#a36d06ecdd6f4c79dc122510eaccc692">00049</a>         <span class="keyword">virtual</span> <span class="keywordtype">double</span> <a class="code" href="classbdm_1_1eEF.html#a36d06ecdd6f4c79dc122510eaccc692" title="Evaluate normalized log-probability.">evallog</a> ( <span class="keyword">const</span> vec &amp;val )<span class="keyword"> const </span>{<span class="keywordtype">double</span> tmp;tmp= <a class="code" href="classbdm_1_1eEF.html#a4135778ecd9ab774762936c82a097c6" title="Evaluate normalized log-probability.">evallog_nn</a> ( val )-<a class="code" href="classbdm_1_1eEF.html#cd678fc9b02007a4b8d6692e746f1bf8" title="logarithm of the normalizing constant, ">lognc</a>();it_assert_debug ( std::isfinite ( tmp ),<span class="stringliteral">"Infinite value"</span> ); <span class="keywordflow">return</span> tmp;}
-<a name="l00051"></a><a class="code" href="classbdm_1_1eEF.html#79a7c8ea8c02e45d410bd1d7ffd72b41">00051</a>         <span class="keyword">virtual</span> vec <a class="code" href="classbdm_1_1eEF.html#79a7c8ea8c02e45d410bd1d7ffd72b41" title="Evaluate normalized log-probability for many samples.">evallog</a> ( <span class="keyword">const</span> mat &amp;Val )<span class="keyword"> const </span>{
-<a name="l00052"></a>00052                 vec x ( Val.cols() );
-<a name="l00053"></a>00053                 <span class="keywordflow">for</span> ( <span class="keywordtype">int</span> i=0;i&lt;Val.cols();i++ ) {x ( i ) =<a class="code" href="classbdm_1_1eEF.html#a4135778ecd9ab774762936c82a097c6" title="Evaluate normalized log-probability.">evallog_nn</a> ( Val.get_col ( i ) ) ;}
-<a name="l00054"></a>00054                 <span class="keywordflow">return</span> x-<a class="code" href="classbdm_1_1eEF.html#cd678fc9b02007a4b8d6692e746f1bf8" title="logarithm of the normalizing constant, ">lognc</a>();
-<a name="l00055"></a>00055         }
-<a name="l00057"></a><a class="code" href="classbdm_1_1eEF.html#cf38af29e8e3d650c640509a52396053">00057</a>         <span class="keyword">virtual</span> <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1eEF.html#cf38af29e8e3d650c640509a52396053" title="Power of the density, used e.g. to flatten the density.">pow</a> ( <span class="keywordtype">double</span> p ) {it_error ( <span class="stringliteral">"Not implemented"</span> );};
-<a name="l00058"></a>00058 };
-<a name="l00059"></a>00059 
-<a name="l00066"></a><a class="code" href="classbdm_1_1mEF.html">00066</a> <span class="keyword">class </span><a class="code" href="classbdm_1_1mEF.html" title="Exponential family model.">mEF</a> : <span class="keyword">public</span> <a class="code" href="classbdm_1_1mpdf.html" title="Conditional probability density, e.g. modeling some dependencies.">mpdf</a> {
-<a name="l00067"></a>00067 
-<a name="l00068"></a>00068 <span class="keyword">public</span>:
-<a name="l00070"></a><a class="code" href="classbdm_1_1mEF.html#dd7caad7026b778af66e34480eec6f9e">00070</a>         <a class="code" href="classbdm_1_1mEF.html#dd7caad7026b778af66e34480eec6f9e" title="Default constructor.">mEF</a> ( ) :<a class="code" href="classbdm_1_1mpdf.html" title="Conditional probability density, e.g. modeling some dependencies.">mpdf</a> ( ) {};
-<a name="l00071"></a>00071 };
-<a name="l00072"></a>00072 
-<a name="l00074"></a><a class="code" href="classbdm_1_1BMEF.html">00074</a> <span class="keyword">class </span><a class="code" href="classbdm_1_1BMEF.html" title="Estimator for Exponential family.">BMEF</a> : <span class="keyword">public</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> {
-<a name="l00075"></a>00075 <span class="keyword">protected</span>:
-<a name="l00077"></a><a class="code" href="classbdm_1_1BMEF.html#1331865e10fb1ccef65bb4c47fa3be64">00077</a>         <span class="keywordtype">double</span> <a class="code" href="classbdm_1_1BMEF.html#1331865e10fb1ccef65bb4c47fa3be64" title="forgetting factor">frg</a>;
-<a name="l00079"></a><a class="code" href="classbdm_1_1BMEF.html#06e7b3ac03e10017d4288c76888e2865">00079</a>         <span class="keywordtype">double</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>;
-<a name="l00080"></a>00080 <span class="keyword">public</span>:
-<a name="l00082"></a><a class="code" href="classbdm_1_1BMEF.html#2def512872ed8a4fc3b702371ec0be55">00082</a>         <a class="code" href="classbdm_1_1BMEF.html#2def512872ed8a4fc3b702371ec0be55" title="Default constructor (=empty constructor).">BMEF</a> ( <span class="keywordtype">double</span> frg0=1.0 ) :<a class="code" href="classbdm_1_1BM.html" title="Bayesian Model of a system, i.e. all uncertainty is modeled by probabilities.">BM</a> ( ), <a class="code" href="classbdm_1_1BMEF.html#1331865e10fb1ccef65bb4c47fa3be64" title="forgetting factor">frg</a> ( frg0 ) {}
-<a name="l00084"></a><a class="code" href="classbdm_1_1BMEF.html#9662379513101405e159e76717104e62">00084</a>         <a class="code" href="classbdm_1_1BMEF.html#9662379513101405e159e76717104e62" title="Copy constructor.">BMEF</a> ( <span class="keyword">const</span> <a class="code" href="classbdm_1_1BMEF.html" title="Estimator for Exponential family.">BMEF</a> &amp;B ) :<a class="code" href="classbdm_1_1BM.html" title="Bayesian Model of a system, i.e. all uncertainty is modeled by probabilities.">BM</a> ( B ), <a class="code" href="classbdm_1_1BMEF.html#1331865e10fb1ccef65bb4c47fa3be64" title="forgetting factor">frg</a> ( B.<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> ( B.<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="l00086"></a><a class="code" href="classbdm_1_1BMEF.html#d2b528b7a41ca67163152142f5404051">00086</a>         <span class="keyword">virtual</span> <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1BMEF.html#d2b528b7a41ca67163152142f5404051" title="get statistics from another model">set_statistics</a> ( <span class="keyword">const</span> <a class="code" href="classbdm_1_1BMEF.html" title="Estimator for Exponential family.">BMEF</a>* BM0 ) {it_error ( <span class="stringliteral">"Not implemented"</span> );};
-<a name="l00088"></a><a class="code" href="classbdm_1_1BMEF.html#bf58deb99af2a6cc674f13ff90300de6">00088</a>         <span class="keyword">virtual</span> <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1BMEF.html#bf58deb99af2a6cc674f13ff90300de6" title="Weighted update of sufficient statistics (Bayes rule).">bayes</a> ( <span class="keyword">const</span> vec &amp;data, <span class="keyword">const</span> <span class="keywordtype">double</span> w ) {};
-<a name="l00089"></a>00089         <span class="comment">//original Bayes</span>
-<a name="l00090"></a>00090         <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1BMEF.html#bf58deb99af2a6cc674f13ff90300de6" title="Weighted update of sufficient statistics (Bayes rule).">bayes</a> ( <span class="keyword">const</span> vec &amp;dt );
-<a name="l00092"></a><a class="code" href="classbdm_1_1BMEF.html#b2916a2e71a958665054473124d5e749">00092</a>         <span class="keyword">virtual</span> <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1BMEF.html#b2916a2e71a958665054473124d5e749" 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 ) {it_error ( <span class="stringliteral">"Not implemented"</span> );}
-<a name="l00094"></a>00094 <span class="comment">//      virtual void flatten ( double nu0 ) {it_error ( "Not implemented" );}</span>
-<a name="l00095"></a>00095 
-<a name="l00096"></a><a class="code" href="classbdm_1_1BMEF.html#62d2e4691bed41a1efa6b9c2e35e5c67">00096</a>         <a class="code" href="classbdm_1_1BMEF.html" title="Estimator for Exponential family.">BMEF</a>* <a class="code" href="classbdm_1_1BMEF.html#62d2e4691bed41a1efa6b9c2e35e5c67" title="Flatten the posterior as if to keep nu0 data.">_copy_</a> ()<span class="keyword"> const </span>{it_error ( <span class="stringliteral">"function _copy_ not implemented for this BM"</span> ); <span class="keywordflow">return</span> NULL;};
-<a name="l00097"></a>00097 };
-<a name="l00098"></a>00098 
-<a name="l00099"></a>00099 <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt;
-<a name="l00100"></a>00100 <span class="keyword">class </span>mlnorm;
-<a name="l00101"></a>00101 
-<a name="l00107"></a>00107 <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt;
-<a name="l00108"></a><a class="code" href="classbdm_1_1enorm.html">00108</a> <span class="keyword">class </span><a class="code" href="classbdm_1_1enorm.html" title="Gaussian density with positive definite (decomposed) covariance matrix.">enorm</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="l00109"></a>00109 <span class="keyword">protected</span>:
-<a name="l00111"></a><a class="code" href="classbdm_1_1enorm.html#c702a194720853570d08b65482f842c7">00111</a>         vec <a class="code" href="classbdm_1_1enorm.html#c702a194720853570d08b65482f842c7" title="mean value">mu</a>;
-<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 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="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="l00024"></a>00024 
+<a name="l00026"></a>00026         <span class="keyword">extern</span> Uniform_RNG UniRNG;
+<a name="l00028"></a>00028         <span class="keyword">extern</span> Normal_RNG NorRNG;
+<a name="l00030"></a>00030         <span class="keyword">extern</span> <a class="code" href="classitpp_1_1Gamma__RNG.html" title="Gamma distribution.">Gamma_RNG</a> GamRNG;
+<a name="l00031"></a>00031 
+<a name="l00038"></a><a class="code" href="classbdm_1_1eEF.html">00038</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>
+<a name="l00039"></a>00039         {
+<a name="l00040"></a>00040                 <span class="keyword">public</span>:
+<a name="l00041"></a>00041 <span class="comment">//      eEF() :epdf() {};</span>
+<a name="l00043"></a><a class="code" href="classbdm_1_1eEF.html#d5459d472d0feca7cf1fb5f65c4b9ef4">00043</a> <span class="comment"></span>                        <a class="code" href="classbdm_1_1eEF.html#d5459d472d0feca7cf1fb5f65c4b9ef4" title="default constructor">eEF</a> ( ) :<a class="code" href="classbdm_1_1epdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a> ( ) {};
+<a name="l00045"></a>00045                         <span class="keyword">virtual</span> <span class="keywordtype">double</span> <a class="code" href="classbdm_1_1eEF.html#cd678fc9b02007a4b8d6692e746f1bf8" title="logarithm of the normalizing constant, ">lognc</a>() <span class="keyword">const</span> =0;
+<a name="l00047"></a><a class="code" href="classbdm_1_1eEF.html#deef7d6273ba4d5a5cf0bbd91ec7277a">00047</a>                         <span class="keyword">virtual</span> <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1eEF.html#deef7d6273ba4d5a5cf0bbd91ec7277a" title="TODO decide if it is really needed.">dupdate</a> ( mat &amp;v ) {it_error ( <span class="stringliteral">"Not implemented"</span> );};
+<a name="l00049"></a><a class="code" href="classbdm_1_1eEF.html#a4135778ecd9ab774762936c82a097c6">00049</a>                         <span class="keyword">virtual</span> <span class="keywordtype">double</span> <a class="code" href="classbdm_1_1eEF.html#a4135778ecd9ab774762936c82a097c6" title="Evaluate normalized log-probability.">evallog_nn</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="l00051"></a><a class="code" href="classbdm_1_1eEF.html#a36d06ecdd6f4c79dc122510eaccc692">00051</a>                         <span class="keyword">virtual</span> <span class="keywordtype">double</span> <a class="code" href="classbdm_1_1eEF.html#a36d06ecdd6f4c79dc122510eaccc692" title="Evaluate normalized log-probability.">evallog</a> ( <span class="keyword">const</span> vec &amp;val )<span class="keyword"> const </span>{<span class="keywordtype">double</span> tmp;tmp= <a class="code" href="classbdm_1_1eEF.html#a4135778ecd9ab774762936c82a097c6" title="Evaluate normalized log-probability.">evallog_nn</a> ( val )-<a class="code" href="classbdm_1_1eEF.html#cd678fc9b02007a4b8d6692e746f1bf8" title="logarithm of the normalizing constant, ">lognc</a>();it_assert_debug ( std::isfinite ( tmp ),<span class="stringliteral">"Infinite value"</span> ); <span class="keywordflow">return</span> tmp;}
+<a name="l00053"></a><a class="code" href="classbdm_1_1eEF.html#79a7c8ea8c02e45d410bd1d7ffd72b41">00053</a>                         <span class="keyword">virtual</span> vec <a class="code" href="classbdm_1_1eEF.html#a36d06ecdd6f4c79dc122510eaccc692" title="Evaluate normalized log-probability.">evallog</a> ( <span class="keyword">const</span> mat &amp;Val )<span class="keyword"> const</span>
+<a name="l00054"></a>00054 <span class="keyword">                        </span>{
+<a name="l00055"></a>00055                                 vec x ( Val.cols() );
+<a name="l00056"></a>00056                                 <span class="keywordflow">for</span> ( <span class="keywordtype">int</span> i=0;i&lt;Val.cols();i++ ) {x ( i ) =<a class="code" href="classbdm_1_1eEF.html#a4135778ecd9ab774762936c82a097c6" title="Evaluate normalized log-probability.">evallog_nn</a> ( Val.get_col ( i ) ) ;}
+<a name="l00057"></a>00057                                 <span class="keywordflow">return</span> x-<a class="code" href="classbdm_1_1eEF.html#cd678fc9b02007a4b8d6692e746f1bf8" title="logarithm of the normalizing constant, ">lognc</a>();
+<a name="l00058"></a>00058                         }
+<a name="l00060"></a><a class="code" href="classbdm_1_1eEF.html#cf38af29e8e3d650c640509a52396053">00060</a>                         <span class="keyword">virtual</span> <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1eEF.html#cf38af29e8e3d650c640509a52396053" title="Power of the density, used e.g. to flatten the density.">pow</a> ( <span class="keywordtype">double</span> p ) {it_error ( <span class="stringliteral">"Not implemented"</span> );};
+<a name="l00061"></a>00061         };
+<a name="l00062"></a>00062 
+<a name="l00069"></a><a class="code" href="classbdm_1_1mEF.html">00069</a>         <span class="keyword">class </span><a class="code" href="classbdm_1_1mEF.html" title="Exponential family model.">mEF</a> : <span class="keyword">public</span> <a class="code" href="classbdm_1_1mpdf.html" title="Conditional probability density, e.g. modeling some dependencies.">mpdf</a>
+<a name="l00070"></a>00070         {
+<a name="l00071"></a>00071 
+<a name="l00072"></a>00072                 <span class="keyword">public</span>:
+<a name="l00074"></a><a class="code" href="classbdm_1_1mEF.html#dd7caad7026b778af66e34480eec6f9e">00074</a>                         <a class="code" href="classbdm_1_1mEF.html#dd7caad7026b778af66e34480eec6f9e" title="Default constructor.">mEF</a> ( ) :<a class="code" href="classbdm_1_1mpdf.html" title="Conditional probability density, e.g. modeling some dependencies.">mpdf</a> ( ) {};
+<a name="l00075"></a>00075         };
+<a name="l00076"></a>00076 
+<a name="l00078"></a><a class="code" href="classbdm_1_1BMEF.html">00078</a>         <span class="keyword">class </span><a class="code" href="classbdm_1_1BMEF.html" title="Estimator for Exponential family.">BMEF</a> : <span class="keyword">public</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>
+<a name="l00079"></a>00079         {
+<a name="l00080"></a>00080                 <span class="keyword">protected</span>:
+<a name="l00082"></a><a class="code" href="classbdm_1_1BMEF.html#1331865e10fb1ccef65bb4c47fa3be64">00082</a>                         <span class="keywordtype">double</span> <a class="code" href="classbdm_1_1BMEF.html#1331865e10fb1ccef65bb4c47fa3be64" title="forgetting factor">frg</a>;
+<a name="l00084"></a><a class="code" href="classbdm_1_1BMEF.html#06e7b3ac03e10017d4288c76888e2865">00084</a>                         <span class="keywordtype">double</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>;
+<a name="l00085"></a>00085                 <span class="keyword">public</span>:
+<a name="l00087"></a><a class="code" href="classbdm_1_1BMEF.html#2def512872ed8a4fc3b702371ec0be55">00087</a>                         <a class="code" href="classbdm_1_1BMEF.html#2def512872ed8a4fc3b702371ec0be55" title="Default constructor (=empty constructor).">BMEF</a> ( <span class="keywordtype">double</span> frg0=1.0 ) :<a class="code" href="classbdm_1_1BM.html" title="Bayesian Model of a system, i.e. all uncertainty is modeled by probabilities.">BM</a> ( ), <a class="code" href="classbdm_1_1BMEF.html#1331865e10fb1ccef65bb4c47fa3be64" title="forgetting factor">frg</a> ( frg0 ) {}
+<a name="l00089"></a><a class="code" href="classbdm_1_1BMEF.html#9662379513101405e159e76717104e62">00089</a>                         <a class="code" href="classbdm_1_1BMEF.html#9662379513101405e159e76717104e62" title="Copy constructor.">BMEF</a> ( <span class="keyword">const</span> <a class="code" href="classbdm_1_1BMEF.html" title="Estimator for Exponential family.">BMEF</a> &amp;B ) :<a class="code" href="classbdm_1_1BM.html" title="Bayesian Model of a system, i.e. all uncertainty is modeled by probabilities.">BM</a> ( B ), <a class="code" href="classbdm_1_1BMEF.html#1331865e10fb1ccef65bb4c47fa3be64" title="forgetting factor">frg</a> ( B.<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> ( B.<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="l00091"></a><a class="code" href="classbdm_1_1BMEF.html#d2b528b7a41ca67163152142f5404051">00091</a>                         <span class="keyword">virtual</span> <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1BMEF.html#d2b528b7a41ca67163152142f5404051" title="get statistics from another model">set_statistics</a> ( <span class="keyword">const</span> <a class="code" href="classbdm_1_1BMEF.html" title="Estimator for Exponential family.">BMEF</a>* BM0 ) {it_error ( <span class="stringliteral">"Not implemented"</span> );};
+<a name="l00093"></a><a class="code" href="classbdm_1_1BMEF.html#bf58deb99af2a6cc674f13ff90300de6">00093</a>                         <span class="keyword">virtual</span> <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1BMEF.html#bf58deb99af2a6cc674f13ff90300de6" title="Weighted update of sufficient statistics (Bayes rule).">bayes</a> ( <span class="keyword">const</span> vec &amp;data, <span class="keyword">const</span> <span class="keywordtype">double</span> w ) {};
+<a name="l00094"></a>00094                         <span class="comment">//original Bayes</span>
+<a name="l00095"></a>00095                         <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1BMEF.html#bf58deb99af2a6cc674f13ff90300de6" title="Weighted update of sufficient statistics (Bayes rule).">bayes</a> ( <span class="keyword">const</span> vec &amp;dt );
+<a name="l00097"></a><a class="code" href="classbdm_1_1BMEF.html#b2916a2e71a958665054473124d5e749">00097</a>                         <span class="keyword">virtual</span> <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1BMEF.html#b2916a2e71a958665054473124d5e749" 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 ) {it_error ( <span class="stringliteral">"Not implemented"</span> );}
+<a name="l00099"></a>00099 <span class="comment">//      virtual void flatten ( double nu0 ) {it_error ( "Not implemented" );}</span>
+<a name="l00100"></a>00100 
+<a name="l00101"></a><a class="code" href="classbdm_1_1BMEF.html#62d2e4691bed41a1efa6b9c2e35e5c67">00101</a>                         <a class="code" href="classbdm_1_1BMEF.html" title="Estimator for Exponential family.">BMEF</a>* <a class="code" href="classbdm_1_1BMEF.html#62d2e4691bed41a1efa6b9c2e35e5c67" title="Flatten the posterior as if to keep nu0 data.">_copy_</a> ()<span class="keyword"> const </span>{it_error ( <span class="stringliteral">"function _copy_ not implemented for this BM"</span> ); <span class="keywordflow">return</span> NULL;};
+<a name="l00102"></a>00102         };
+<a name="l00103"></a>00103 
+<a name="l00104"></a>00104         <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt;
+<a name="l00105"></a>00105         <span class="keyword">class </span>mlnorm;
+<a name="l00106"></a>00106 
+<a name="l00112"></a>00112         <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt;
+<a name="l00113"></a><a class="code" href="classbdm_1_1enorm.html">00113</a>         <span class="keyword">class </span><a class="code" href="classbdm_1_1enorm.html" title="Gaussian density with positive definite (decomposed) covariance matrix.">enorm</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="l00114"></a>00114         {
+<a name="l00115"></a>00115                 <span class="keyword">protected</span>:
+<a name="l00117"></a><a class="code" href="classbdm_1_1enorm.html#c702a194720853570d08b65482f842c7">00117</a>                         vec <a class="code" href="classbdm_1_1enorm.html#c702a194720853570d08b65482f842c7" title="mean value">mu</a>;
+<a name="l00119"></a><a class="code" href="classbdm_1_1enorm.html#2d92dde696b2a7a5b10ddef5d22ba2c2">00119</a>                         sq_T <a class="code" href="classbdm_1_1enorm.html#2d92dde696b2a7a5b10ddef5d22ba2c2" title="Covariance matrix in decomposed form.">R</a>;
+<a name="l00120"></a>00120                 <span class="keyword">public</span>:
+<a name="l00123"></a>00123 
+<a name="l00124"></a>00124                         <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="l00125"></a>00125                         <a class="code" href="classbdm_1_1enorm.html" title="Gaussian density with positive definite (decomposed) covariance matrix.">enorm</a> ( <span class="keyword">const</span> vec &amp;<a class="code" href="classbdm_1_1enorm.html#c702a194720853570d08b65482f842c7" title="mean value">mu</a>,<span class="keyword">const</span> sq_T &amp;<a class="code" href="classbdm_1_1enorm.html#2d92dde696b2a7a5b10ddef5d22ba2c2" title="Covariance matrix in decomposed form.">R</a> ) {set_parameters ( mu,R );}
+<a name="l00126"></a>00126                         <span class="keywordtype">void</span> set_parameters ( <span class="keyword">const</span> vec &amp;<a class="code" href="classbdm_1_1enorm.html#c702a194720853570d08b65482f842c7" title="mean value">mu</a>,<span class="keyword">const</span> sq_T &amp;<a class="code" href="classbdm_1_1enorm.html#2d92dde696b2a7a5b10ddef5d22ba2c2" title="Covariance matrix in decomposed form.">R</a> );
 <a name="l00128"></a>00128 
-<a name="l00129"></a>00129         vec <a class="code" href="classbdm_1_1enorm.html#e1a48f52351ec3a349bd443b713b1766" title="Returns a sample,  from density .">sample</a>() <span class="keyword">const</span>;
-<a name="l00130"></a>00130         mat <a class="code" href="classbdm_1_1enorm.html#e1a48f52351ec3a349bd443b713b1766" title="Returns a sample,  from density .">sample</a> ( <span class="keywordtype">int</span> N ) <span class="keyword">const</span>;
-<a name="l00131"></a>00131         <span class="keywordtype">double</span> <a class="code" href="classbdm_1_1enorm.html#e13aeed5b543b2179bacdc4fa2ae47a3" title="Evaluate normalized log-probability.">evallog_nn</a> ( <span class="keyword">const</span> vec &amp;val ) <span class="keyword">const</span>;
-<a name="l00132"></a>00132         <span class="keywordtype">double</span> <a class="code" href="classbdm_1_1enorm.html#25785343aff102cc5df1cab08ba16d32" title="logarithm of the normalizing constant, ">lognc</a> () <span class="keyword">const</span>;
-<a name="l00133"></a><a class="code" href="classbdm_1_1enorm.html#b2fa2915c35366392fe9bb022ca1a600">00133</a>         vec <a class="code" href="classbdm_1_1enorm.html#b2fa2915c35366392fe9bb022ca1a600" title="return expected value">mean</a>()<span class="keyword"> const </span>{<span class="keywordflow">return</span> <a class="code" href="classbdm_1_1enorm.html#c702a194720853570d08b65482f842c7" title="mean value">mu</a>;}
-<a name="l00134"></a><a class="code" href="classbdm_1_1enorm.html#729c75ef0fa8abae03d58ad1f81e6773">00134</a>         vec <a class="code" href="classbdm_1_1enorm.html#729c75ef0fa8abae03d58ad1f81e6773" title="return expected variance (not covariance!)">variance</a>()<span class="keyword"> const </span>{<span class="keywordflow">return</span> diag ( <a class="code" href="classbdm_1_1enorm.html#2d92dde696b2a7a5b10ddef5d22ba2c2" title="Covariance matrix in decomposed form.">R</a>.to_mat() );}
-<a name="l00135"></a>00135 <span class="comment">//      mlnorm&lt;sq_T&gt;* condition ( const RV &amp;rvn ) const ;</span>
-<a name="l00136"></a>00136         <a class="code" href="classbdm_1_1mpdf.html" title="Conditional probability density, e.g. modeling some dependencies.">mpdf</a>* <a class="code" href="classbdm_1_1enorm.html#baea4d49c657342b58297d68cda16d26" title="Return conditional density on the given RV, the remaining rvs will be in conditioning...">condition</a> ( <span class="keyword">const</span> <a class="code" href="classbdm_1_1RV.html" title="Class representing variables, most often random variables.">RV</a> &amp;rvn ) <span class="keyword">const</span> ;
-<a name="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="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="l00131"></a>00131 
+<a name="l00133"></a>00133                         <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1enorm.html#d2e0d3a1e30ab3ab04df2d0c43ae74a2" title="dupdate in exponential form (not really handy)">dupdate</a> ( mat &amp;v,<span class="keywordtype">double</span> nu=1.0 );
+<a name="l00134"></a>00134 
+<a name="l00135"></a>00135                         vec <a class="code" href="classbdm_1_1enorm.html#e1a48f52351ec3a349bd443b713b1766" title="Returns a sample,  from density .">sample</a>() <span class="keyword">const</span>;
+<a name="l00136"></a>00136                         mat <a class="code" href="classbdm_1_1enorm.html#e1a48f52351ec3a349bd443b713b1766" title="Returns a sample,  from density .">sample</a> ( <span class="keywordtype">int</span> N ) <span class="keyword">const</span>;
+<a name="l00137"></a>00137                         <span class="keywordtype">double</span> <a class="code" href="classbdm_1_1enorm.html#e13aeed5b543b2179bacdc4fa2ae47a3" title="Evaluate normalized log-probability.">evallog_nn</a> ( <span class="keyword">const</span> vec &amp;val ) <span class="keyword">const</span>;
+<a name="l00138"></a>00138                         <span class="keywordtype">double</span> <a class="code" href="classbdm_1_1enorm.html#25785343aff102cc5df1cab08ba16d32" title="logarithm of the normalizing constant, ">lognc</a> () <span class="keyword">const</span>;
+<a name="l00139"></a><a class="code" href="classbdm_1_1enorm.html#b2fa2915c35366392fe9bb022ca1a600">00139</a>                         vec <a class="code" href="classbdm_1_1enorm.html#b2fa2915c35366392fe9bb022ca1a600" title="return expected value">mean</a>()<span class="keyword"> const </span>{<span class="keywordflow">return</span> <a class="code" href="classbdm_1_1enorm.html#c702a194720853570d08b65482f842c7" title="mean value">mu</a>;}
+<a name="l00140"></a><a class="code" href="classbdm_1_1enorm.html#729c75ef0fa8abae03d58ad1f81e6773">00140</a>                         vec <a class="code" href="classbdm_1_1enorm.html#729c75ef0fa8abae03d58ad1f81e6773" title="return expected variance (not covariance!)">variance</a>()<span class="keyword"> const </span>{<span class="keywordflow">return</span> diag ( <a class="code" href="classbdm_1_1enorm.html#2d92dde696b2a7a5b10ddef5d22ba2c2" title="Covariance matrix in decomposed form.">R</a>.to_mat() );}
+<a name="l00141"></a>00141 <span class="comment">//      mlnorm&lt;sq_T&gt;* condition ( const RV &amp;rvn ) const ;</span>
+<a name="l00142"></a>00142                         <a class="code" href="classbdm_1_1mpdf.html" title="Conditional probability density, e.g. modeling some dependencies.">mpdf</a>* <a class="code" href="classbdm_1_1enorm.html#baea4d49c657342b58297d68cda16d26" title="Return conditional density on the given RV, the remaining rvs will be in conditioning...">condition</a> ( <span class="keyword">const</span> <a class="code" href="classbdm_1_1RV.html" title="Class representing variables, most often random variables.">RV</a> &amp;rvn ) <span class="keyword">const</span> ;
+<a name="l00143"></a>00143 <span class="comment">//      enorm&lt;sq_T&gt;* marginal ( const RV &amp;rv ) const;</span>
+<a name="l00144"></a>00144                         <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="l00146"></a>00146 
 <a name="l00149"></a>00149 
-<a name="l00150"></a>00150 };
-<a name="l00151"></a>00151 
-<a name="l00158"></a><a class="code" href="classbdm_1_1egiw.html">00158</a> <span class="keyword">class </span><a class="code" href="classbdm_1_1egiw.html" title="Gauss-inverse-Wishart density stored in LD form.">egiw</a> : <span class="keyword">public</span> <a class="code" href="classbdm_1_1eEF.html" title="General conjugate exponential family posterior density.">eEF</a> {
-<a name="l00159"></a>00159 <span class="keyword">protected</span>:
-<a name="l00161"></a><a class="code" href="classbdm_1_1egiw.html#ae56852845c6af176fd9017dbebbbd52">00161</a>         <a class="code" href="classldmat.html" title="Matrix stored in LD form, (commonly known as UD).">ldmat</a> <a class="code" href="classbdm_1_1egiw.html#ae56852845c6af176fd9017dbebbbd52" title="Extended information matrix of sufficient statistics.">V</a>;
-<a name="l00163"></a><a class="code" href="classbdm_1_1egiw.html#447eacf19d4f4083872686f044814dc4">00163</a>         <span class="keywordtype">double</span> <a class="code" href="classbdm_1_1egiw.html#447eacf19d4f4083872686f044814dc4" title="Number of data records (degrees of freedom) of sufficient statistics.">nu</a>;
-<a name="l00165"></a><a class="code" href="classbdm_1_1egiw.html#23e4d78bea7e98840f3da30e76a2b57a">00165</a>         <span class="keywordtype">int</span> <a class="code" href="classbdm_1_1egiw.html#23e4d78bea7e98840f3da30e76a2b57a" title="Dimension of the output.">dimx</a>;
-<a name="l00167"></a><a class="code" href="classbdm_1_1egiw.html#322414c32d9a21a006a5aab0311f64fd">00167</a>         <span class="keywordtype">int</span> <a class="code" href="classbdm_1_1egiw.html#322414c32d9a21a006a5aab0311f64fd" title="Dimension of the regressor.">nPsi</a>;
-<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="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_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 +<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                 }
-<a name="l00184"></a>00184                 <span class="keywordflow">else</span> {
-<a name="l00185"></a>00185                         <a class="code" href="classbdm_1_1egiw.html#447eacf19d4f4083872686f044814dc4" title="Number of data records (degrees of freedom) of sufficient statistics.">nu</a>=nu0;
-<a name="l00186"></a>00186                 }
-<a name="l00187"></a>00187         }
-<a name="l00189"></a>00189 
-<a name="l00190"></a>00190         vec <a class="code" href="classbdm_1_1egiw.html#920f21548b7a3723923dd108fe514c61" title="Returns a sample,  from density .">sample</a>() <span class="keyword">const</span>;
-<a name="l00191"></a>00191         vec <a class="code" href="classbdm_1_1egiw.html#df70c05f918c3a6f86d60f10c1fd6ba2" title="return expected value">mean</a>() <span class="keyword">const</span>;
-<a name="l00192"></a>00192         vec <a class="code" href="classbdm_1_1egiw.html#c1ecc406613cc2341225dc10c3d3b46a" title="return expected variance (not covariance!)">variance</a>() <span class="keyword">const</span>;
-<a name="l00193"></a>00193         <span class="keywordtype">void</span> mean_mat ( mat &amp;M, mat&amp;R ) <span class="keyword">const</span>;
-<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 ) {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> 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>;}
-<a name="l00207"></a>00207 };
+<a name="l00150"></a>00150                         vec&amp; _mu() {<span class="keywordflow">return</span> <a class="code" href="classbdm_1_1enorm.html#c702a194720853570d08b65482f842c7" title="mean value">mu</a>;}
+<a name="l00151"></a>00151                         <span class="keywordtype">void</span> set_mu ( <span class="keyword">const</span> vec mu0 ) { <a class="code" href="classbdm_1_1enorm.html#c702a194720853570d08b65482f842c7" title="mean value">mu</a>=mu0;}
+<a name="l00152"></a>00152                         sq_T&amp; _R() {<span class="keywordflow">return</span> <a class="code" href="classbdm_1_1enorm.html#2d92dde696b2a7a5b10ddef5d22ba2c2" title="Covariance matrix in decomposed form.">R</a>;}
+<a name="l00153"></a>00153                         <span class="keyword">const</span> sq_T&amp; _R()<span class="keyword"> const </span>{<span class="keywordflow">return</span> <a class="code" href="classbdm_1_1enorm.html#2d92dde696b2a7a5b10ddef5d22ba2c2" title="Covariance matrix in decomposed form.">R</a>;}
+<a name="l00155"></a>00155 
+<a name="l00156"></a>00156         };
+<a name="l00157"></a>00157 
+<a name="l00164"></a><a class="code" href="classbdm_1_1egiw.html">00164</a>         <span class="keyword">class </span><a class="code" href="classbdm_1_1egiw.html" title="Gauss-inverse-Wishart density stored in LD form.">egiw</a> : <span class="keyword">public</span> <a class="code" href="classbdm_1_1eEF.html" title="General conjugate exponential family posterior density.">eEF</a>
+<a name="l00165"></a>00165         {
+<a name="l00166"></a>00166                 <span class="keyword">protected</span>:
+<a name="l00168"></a><a class="code" href="classbdm_1_1egiw.html#ae56852845c6af176fd9017dbebbbd52">00168</a>                         <a class="code" href="classldmat.html" title="Matrix stored in LD form, (commonly known as UD).">ldmat</a> <a class="code" href="classbdm_1_1egiw.html#ae56852845c6af176fd9017dbebbbd52" title="Extended information matrix of sufficient statistics.">V</a>;
+<a name="l00170"></a><a class="code" href="classbdm_1_1egiw.html#447eacf19d4f4083872686f044814dc4">00170</a>                         <span class="keywordtype">double</span> <a class="code" href="classbdm_1_1egiw.html#447eacf19d4f4083872686f044814dc4" title="Number of data records (degrees of freedom) of sufficient statistics.">nu</a>;
+<a name="l00172"></a><a class="code" href="classbdm_1_1egiw.html#23e4d78bea7e98840f3da30e76a2b57a">00172</a>                         <span class="keywordtype">int</span> <a class="code" href="classbdm_1_1egiw.html#23e4d78bea7e98840f3da30e76a2b57a" title="Dimension of the output.">dimx</a>;
+<a name="l00174"></a><a class="code" href="classbdm_1_1egiw.html#322414c32d9a21a006a5aab0311f64fd">00174</a>                         <span class="keywordtype">int</span> <a class="code" href="classbdm_1_1egiw.html#322414c32d9a21a006a5aab0311f64fd" title="Dimension of the regressor.">nPsi</a>;
+<a name="l00175"></a>00175                 <span class="keyword">public</span>:
+<a name="l00178"></a>00178                         <a class="code" href="classbdm_1_1egiw.html" title="Gauss-inverse-Wishart density stored in LD form.">egiw</a>() :<a class="code" href="classbdm_1_1eEF.html" title="General conjugate exponential family posterior density.">eEF</a>() {};
+<a name="l00179"></a>00179                         <a class="code" href="classbdm_1_1egiw.html" title="Gauss-inverse-Wishart density stored in LD form.">egiw</a> ( <span class="keywordtype">int</span> dimx0, <a class="code" href="classldmat.html" title="Matrix stored in LD form, (commonly known as UD).">ldmat</a> V0, <span class="keywordtype">double</span> nu0=-1.0 ) :<a class="code" href="classbdm_1_1eEF.html" title="General conjugate exponential family posterior density.">eEF</a>() {set_parameters ( dimx0,V0, nu0 );};
+<a name="l00180"></a>00180 
+<a name="l00181"></a>00181                         <span class="keywordtype">void</span> set_parameters ( <span class="keywordtype">int</span> dimx0, <a class="code" href="classldmat.html" title="Matrix stored in LD form, (commonly known as UD).">ldmat</a> V0, <span class="keywordtype">double</span> nu0=-1.0 )
+<a name="l00182"></a>00182                         {
+<a name="l00183"></a>00183                                 <a class="code" href="classbdm_1_1egiw.html#23e4d78bea7e98840f3da30e76a2b57a" title="Dimension of the output.">dimx</a>=dimx0;
+<a name="l00184"></a>00184                                 <a class="code" href="classbdm_1_1egiw.html#322414c32d9a21a006a5aab0311f64fd" title="Dimension of the regressor.">nPsi</a> = V0.<a class="code" href="group__math.html#g96dfb21865db4f5bd36fa70f9b0b1163" title="access function">rows</a>()-<a class="code" href="classbdm_1_1egiw.html#23e4d78bea7e98840f3da30e76a2b57a" title="Dimension of the output.">dimx</a>;
+<a name="l00185"></a>00185                                 <a class="code" href="classbdm_1_1epdf.html#16adac20ec7fe07e1ea0b27d917788ce" title="dimension of the random variable">dim</a> = <a class="code" href="classbdm_1_1egiw.html#23e4d78bea7e98840f3da30e76a2b57a" title="Dimension of the output.">dimx</a>* ( <a class="code" href="classbdm_1_1egiw.html#23e4d78bea7e98840f3da30e76a2b57a" title="Dimension of the output.">dimx</a>+<a class="code" href="classbdm_1_1egiw.html#322414c32d9a21a006a5aab0311f64fd" title="Dimension of the regressor.">nPsi</a> ); <span class="comment">// size(R) + size(Theta)</span>
+<a name="l00186"></a>00186 
+<a name="l00187"></a>00187                                 <a class="code" href="classbdm_1_1egiw.html#ae56852845c6af176fd9017dbebbbd52" title="Extended information matrix of sufficient statistics.">V</a>=V0;
+<a name="l00188"></a>00188                                 <span class="keywordflow">if</span> ( nu0&lt;0 )
+<a name="l00189"></a>00189                                 {
+<a name="l00190"></a>00190                                         <a class="code" href="classbdm_1_1egiw.html#447eacf19d4f4083872686f044814dc4" title="Number of data records (degrees of freedom) of sufficient statistics.">nu</a> = 0.1 +<a class="code" href="classbdm_1_1egiw.html#322414c32d9a21a006a5aab0311f64fd" title="Dimension of the regressor.">nPsi</a> +2*<a class="code" href="classbdm_1_1egiw.html#23e4d78bea7e98840f3da30e76a2b57a" title="Dimension of the output.">dimx</a> +2; <span class="comment">// +2 assures finite expected value of R</span>
+<a name="l00191"></a>00191                                         <span class="comment">// terms before that are sufficient for finite normalization</span>
+<a name="l00192"></a>00192                                 }
+<a name="l00193"></a>00193                                 <span class="keywordflow">else</span>
+<a name="l00194"></a>00194                                 {
+<a name="l00195"></a>00195                                         <a class="code" href="classbdm_1_1egiw.html#447eacf19d4f4083872686f044814dc4" title="Number of data records (degrees of freedom) of sufficient statistics.">nu</a>=nu0;
+<a name="l00196"></a>00196                                 }
+<a name="l00197"></a>00197                         }
+<a name="l00199"></a>00199 
+<a name="l00200"></a>00200                         vec <a class="code" href="classbdm_1_1egiw.html#920f21548b7a3723923dd108fe514c61" title="Returns a sample,  from density .">sample</a>() <span class="keyword">const</span>;
+<a name="l00201"></a>00201                         vec <a class="code" href="classbdm_1_1egiw.html#df70c05f918c3a6f86d60f10c1fd6ba2" title="return expected value">mean</a>() <span class="keyword">const</span>;
+<a name="l00202"></a>00202                         vec <a class="code" href="classbdm_1_1egiw.html#c1ecc406613cc2341225dc10c3d3b46a" title="return expected variance (not covariance!)">variance</a>() <span class="keyword">const</span>;
+<a name="l00203"></a>00203                         <span class="keywordtype">void</span> mean_mat ( mat &amp;M, mat&amp;R ) <span class="keyword">const</span>;
+<a name="l00205"></a>00205                         <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="l00206"></a>00206                         <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="l00207"></a><a class="code" href="classbdm_1_1egiw.html#8e610e95401a11baf34f65e16ecd87be">00207</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="l00208"></a>00208 
-<a name="l00217"></a><a class="code" href="classbdm_1_1eDirich.html">00217</a> <span class="keyword">class </span><a class="code" href="classbdm_1_1eDirich.html" title="Dirichlet posterior density.">eDirich</a>: <span class="keyword">public</span> <a class="code" href="classbdm_1_1eEF.html" title="General conjugate exponential family posterior density.">eEF</a> {
-<a name="l00218"></a>00218 <span class="keyword">protected</span>:
-<a name="l00220"></a><a class="code" href="classbdm_1_1eDirich.html#f25886a49b4667af61245de81c83b5d2">00220</a>         vec <a class="code" href="classbdm_1_1eDirich.html#f25886a49b4667af61245de81c83b5d2" title="sufficient statistics">beta</a>;
-<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="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="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#c4378cf8037f6bed29c74eea63344b31" title="Copy constructor.">multiBM</a> ( <span class="keyword">const</span> <a class="code" href="classbdm_1_1multiBM.html" title="Estimator for Multinomial density.">multiBM</a> &amp;B ) : <a class="code" href="classbdm_1_1BMEF.html" title="Estimator for Exponential family.">BMEF</a> ( B ),<a class="code" href="classbdm_1_1multiBM.html#9ecc6878abbd20eb8d8e43b6ab3f941a" title="Conjugate prior and posterior.">est</a> ( B.<a class="code" href="classbdm_1_1multiBM.html#9ecc6878abbd20eb8d8e43b6ab3f941a" title="Conjugate prior and posterior.">est</a> ),<a class="code" href="classbdm_1_1multiBM.html#044263356944c92209eecd39a5187d25" title="Pointer inside est to sufficient statistics.">beta</a> ( <a class="code" href="classbdm_1_1multiBM.html#9ecc6878abbd20eb8d8e43b6ab3f941a" title="Conjugate prior and posterior.">est</a>._beta() ) {}
-<a name="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>(0), <a class="code" href="classbdm_1_1egamma.html#457bfb1ccb2057df85073e519a15ccc1" title="Vector .">beta</a>(0) {};
-<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="l00211"></a>00211 
+<a name="l00212"></a>00212                         <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="l00213"></a>00213                         <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="l00214"></a>00214                         <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="l00215"></a>00215                         <span class="keyword">const</span> <span class="keywordtype">double</span>&amp; _nu()<span class="keyword"> const </span>{<span class="keywordflow">return</span> <a class="code" href="classbdm_1_1egiw.html#447eacf19d4f4083872686f044814dc4" title="Number of data records (degrees of freedom) of sufficient statistics.">nu</a>;}
+<a name="l00217"></a>00217         };
+<a name="l00218"></a>00218 
+<a name="l00227"></a><a class="code" href="classbdm_1_1eDirich.html">00227</a>         <span class="keyword">class </span><a class="code" href="classbdm_1_1eDirich.html" title="Dirichlet posterior density.">eDirich</a>: <span class="keyword">public</span> <a class="code" href="classbdm_1_1eEF.html" title="General conjugate exponential family posterior density.">eEF</a>
+<a name="l00228"></a>00228         {
+<a name="l00229"></a>00229                 <span class="keyword">protected</span>:
+<a name="l00231"></a><a class="code" href="classbdm_1_1eDirich.html#f25886a49b4667af61245de81c83b5d2">00231</a>                         vec <a class="code" href="classbdm_1_1eDirich.html#f25886a49b4667af61245de81c83b5d2" title="sufficient statistics">beta</a>;
+<a name="l00233"></a><a class="code" href="classbdm_1_1eDirich.html#ee9db192a6f0ab7b29c33b2a18a5e1b4">00233</a>                         <span class="keywordtype">double</span> <a class="code" href="classbdm_1_1eDirich.html#ee9db192a6f0ab7b29c33b2a18a5e1b4" title="speedup variable">gamma</a>;
+<a name="l00234"></a>00234                 <span class="keyword">public</span>:
+<a name="l00237"></a>00237 
+<a name="l00238"></a>00238                         <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="l00239"></a>00239                         <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="l00240"></a>00240                         eDirich ( <span class="keyword">const</span> vec &amp;beta0 ) {set_parameters ( beta0 );};
+<a name="l00241"></a>00241                         <span class="keywordtype">void</span> set_parameters ( <span class="keyword">const</span> vec &amp;beta0 )
+<a name="l00242"></a>00242                         {
+<a name="l00243"></a>00243                                 <a class="code" href="classbdm_1_1eDirich.html#f25886a49b4667af61245de81c83b5d2" title="sufficient statistics">beta</a>= beta0;
+<a name="l00244"></a>00244                                 <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="l00245"></a>00245                                 <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="l00246"></a>00246                         }
+<a name="l00248"></a>00248 
+<a name="l00249"></a><a class="code" href="classbdm_1_1eDirich.html#3290613d31d58daa8a45a54b003871fc">00249</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="l00250"></a><a class="code" href="classbdm_1_1eDirich.html#cb343355ec791298bb5a3404cd482fb6">00250</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="l00251"></a><a class="code" href="classbdm_1_1eDirich.html#43c547a2507e233706f92712d8c2aacc">00251</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="l00253"></a><a class="code" href="classbdm_1_1eDirich.html#e09a24938e80c3d94b0ee842d1552318">00253</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="l00254"></a>00254 <span class="keyword">                        </span>{
+<a name="l00255"></a>00255                                 <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="l00256"></a>00256                                 <span class="keywordflow">return</span> tmp;
+<a name="l00257"></a>00257                         };
+<a name="l00258"></a><a class="code" href="classbdm_1_1eDirich.html#279a99f6266c82fe2273e83841f19eb2">00258</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="l00259"></a>00259 <span class="keyword">                        </span>{
+<a name="l00260"></a>00260                                 <span class="keywordtype">double</span> tmp;
+<a name="l00261"></a>00261                                 <span class="keywordtype">double</span> gam=sum ( <a class="code" href="classbdm_1_1eDirich.html#f25886a49b4667af61245de81c83b5d2" title="sufficient statistics">beta</a> );
+<a name="l00262"></a>00262                                 <span class="keywordtype">double</span> lgb=0.0;
+<a name="l00263"></a>00263                                 <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="l00264"></a>00264                                 tmp= lgb-lgamma ( gam );
+<a name="l00265"></a>00265                                 it_assert_debug ( std::isfinite ( tmp ),<span class="stringliteral">"Infinite value"</span> );
+<a name="l00266"></a>00266                                 <span class="keywordflow">return</span> tmp;
+<a name="l00267"></a>00267                         };
+<a name="l00269"></a><a class="code" href="classbdm_1_1eDirich.html#175e0add26d2105c28d8121eefb9e324">00269</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="l00271"></a>00271         };
+<a name="l00272"></a>00272 
+<a name="l00274"></a><a class="code" href="classbdm_1_1multiBM.html">00274</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="l00275"></a>00275         {
+<a name="l00276"></a>00276                 <span class="keyword">protected</span>:
+<a name="l00278"></a><a class="code" href="classbdm_1_1multiBM.html#9ecc6878abbd20eb8d8e43b6ab3f941a">00278</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="l00280"></a><a class="code" href="classbdm_1_1multiBM.html#044263356944c92209eecd39a5187d25">00280</a>                         vec &amp;<a class="code" href="classbdm_1_1multiBM.html#044263356944c92209eecd39a5187d25" title="Pointer inside est to sufficient statistics.">beta</a>;
+<a name="l00281"></a>00281                 <span class="keyword">public</span>:
+<a name="l00283"></a><a class="code" href="classbdm_1_1multiBM.html#c4dd6d9522a8a605776d21bac9bd9daf">00283</a>                         <a class="code" href="classbdm_1_1multiBM.html#c4dd6d9522a8a605776d21bac9bd9daf" title="Default constructor.">multiBM</a> ( ) : <a class="code" href="classbdm_1_1BMEF.html" title="Estimator for Exponential family.">BMEF</a> ( ),<a class="code" href="classbdm_1_1multiBM.html#9ecc6878abbd20eb8d8e43b6ab3f941a" title="Conjugate prior and posterior.">est</a> ( ),<a class="code" href="classbdm_1_1multiBM.html#044263356944c92209eecd39a5187d25" title="Pointer inside est to sufficient statistics.">beta</a> ( <a class="code" href="classbdm_1_1multiBM.html#9ecc6878abbd20eb8d8e43b6ab3f941a" title="Conjugate prior and posterior.">est</a>._beta() )
+<a name="l00284"></a>00284                         {
+<a name="l00285"></a>00285                                 <span class="keywordflow">if</span> ( <a class="code" href="classbdm_1_1multiBM.html#044263356944c92209eecd39a5187d25" title="Pointer inside est to sufficient statistics.">beta</a>.length() &gt;0 ) {<a class="code" href="classbdm_1_1BMEF.html#06e7b3ac03e10017d4288c76888e2865" title="cached value of lognc() in the previous step (used in evaluation of ll )">last_lognc</a>=<a class="code" href="classbdm_1_1multiBM.html#9ecc6878abbd20eb8d8e43b6ab3f941a" title="Conjugate prior and posterior.">est</a>.<a class="code" href="classbdm_1_1eDirich.html#279a99f6266c82fe2273e83841f19eb2" title="logarithm of the normalizing constant, ">lognc</a>();}
+<a name="l00286"></a>00286                                 <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="l00287"></a>00287                         }
+<a name="l00289"></a><a class="code" href="classbdm_1_1multiBM.html#c4378cf8037f6bed29c74eea63344b31">00289</a>                         <a class="code" href="classbdm_1_1multiBM.html#c4378cf8037f6bed29c74eea63344b31" title="Copy constructor.">multiBM</a> ( <span class="keyword">const</span> <a class="code" href="classbdm_1_1multiBM.html" title="Estimator for Multinomial density.">multiBM</a> &amp;B ) : <a class="code" href="classbdm_1_1BMEF.html" title="Estimator for Exponential family.">BMEF</a> ( B ),<a class="code" href="classbdm_1_1multiBM.html#9ecc6878abbd20eb8d8e43b6ab3f941a" title="Conjugate prior and posterior.">est</a> ( B.<a class="code" href="classbdm_1_1multiBM.html#9ecc6878abbd20eb8d8e43b6ab3f941a" title="Conjugate prior and posterior.">est</a> ),<a class="code" href="classbdm_1_1multiBM.html#044263356944c92209eecd39a5187d25" title="Pointer inside est to sufficient statistics.">beta</a> ( <a class="code" href="classbdm_1_1multiBM.html#9ecc6878abbd20eb8d8e43b6ab3f941a" title="Conjugate prior and posterior.">est</a>._beta() ) {}
+<a name="l00291"></a><a class="code" href="classbdm_1_1multiBM.html#dbe6b90d410dc062a233d1dc09eeba52">00291</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="l00292"></a><a class="code" href="classbdm_1_1multiBM.html#1e4bf41b61937fd80f34049742e23f95">00292</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="l00293"></a>00293                         {
+<a name="l00294"></a>00294                                 <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="l00295"></a>00295                                 <a class="code" href="classbdm_1_1multiBM.html#044263356944c92209eecd39a5187d25" title="Pointer inside est to sufficient statistics.">beta</a>+=dt;
+<a name="l00296"></a>00296                                 <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="l00297"></a>00297                         }
+<a name="l00298"></a><a class="code" href="classbdm_1_1multiBM.html#e157b607c1e3fa91d42aeea44458e2bf">00298</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="l00299"></a>00299 <span class="keyword">                        </span>{
+<a name="l00300"></a>00300                                 <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="l00301"></a>00301                                 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="l00302"></a>00302 
+<a name="l00303"></a>00303                                 <span class="keywordtype">double</span> lll;
+<a name="l00304"></a>00304                                 <span class="keywordflow">if</span> ( <a class="code" href="classbdm_1_1BMEF.html#1331865e10fb1ccef65bb4c47fa3be64" title="forgetting factor">frg</a>&lt;1.0 )
+<a name="l00305"></a>00305                                         {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="l00306"></a>00306                                 <span class="keywordflow">else</span>
+<a name="l00307"></a>00307                                         <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="l00308"></a>00308                                         <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="l00309"></a>00309 
+<a name="l00310"></a>00310                                 beta+=dt;
+<a name="l00311"></a>00311                                 <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="l00312"></a>00312                         }
+<a name="l00313"></a><a class="code" href="classbdm_1_1multiBM.html#aaeb18c989088feb8d26d300e4971732">00313</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="l00314"></a>00314                         {
+<a name="l00315"></a>00315                                 <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="l00316"></a>00316                                 <span class="comment">// sum(beta) should be equal to sum(B.beta)</span>
+<a name="l00317"></a>00317                                 <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="l00318"></a>00318                                 <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="l00319"></a>00319                                 <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="l00320"></a>00320                         }
+<a name="l00321"></a>00321                         <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="l00322"></a>00322                         <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="l00323"></a>00323                         <span class="keywordtype">void</span> set_parameters ( <span class="keyword">const</span> vec &amp;beta0 )
+<a name="l00324"></a>00324                         {
+<a name="l00325"></a>00325                                 <a class="code" href="classbdm_1_1multiBM.html#9ecc6878abbd20eb8d8e43b6ab3f941a" title="Conjugate prior and posterior.">est</a>.set_parameters ( beta0 );
+<a name="l00326"></a>00326                                 <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="l00327"></a>00327                         }
+<a name="l00328"></a>00328         };
 <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="l00358"></a><a class="code" href="classbdm_1_1eigamma.html">00358</a> <span class="keyword">class </span><a class="code" href="classbdm_1_1eigamma.html" title="Inverse-Gamma posterior density.">eigamma</a> : <span class="keyword">public</span> <a class="code" href="classbdm_1_1egamma.html" title="Gamma posterior density.">egamma</a> {
-<a name="l00359"></a>00359 <span class="keyword">protected</span>:
-<a name="l00360"></a>00360 <span class="keyword">public</span> :
+<a name="l00339"></a><a class="code" href="classbdm_1_1egamma.html">00339</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="l00340"></a>00340         {
+<a name="l00341"></a>00341                 <span class="keyword">protected</span>:
+<a name="l00343"></a><a class="code" href="classbdm_1_1egamma.html#0901ec983e66b8337aaa506e13b122fa">00343</a>                         vec <a class="code" href="classbdm_1_1egamma.html#0901ec983e66b8337aaa506e13b122fa" title="Vector .">alpha</a>;
+<a name="l00345"></a><a class="code" href="classbdm_1_1egamma.html#457bfb1ccb2057df85073e519a15ccc1">00345</a>                         vec <a class="code" href="classbdm_1_1egamma.html#457bfb1ccb2057df85073e519a15ccc1" title="Vector .">beta</a>;
+<a name="l00346"></a>00346                 <span class="keyword">public</span> :
+<a name="l00349"></a>00349                         <a class="code" href="classbdm_1_1egamma.html" title="Gamma posterior density.">egamma</a> ( ) :<a class="code" href="classbdm_1_1eEF.html" title="General conjugate exponential family posterior density.">eEF</a> ( ), <a class="code" href="classbdm_1_1egamma.html#0901ec983e66b8337aaa506e13b122fa" title="Vector .">alpha</a> ( 0 ), <a class="code" href="classbdm_1_1egamma.html#457bfb1ccb2057df85073e519a15ccc1" title="Vector .">beta</a> ( 0 ) {};
+<a name="l00350"></a>00350                         <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="l00351"></a>00351                         <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="l00353"></a>00353 
+<a name="l00354"></a>00354                         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="l00356"></a>00356 <span class="comment">//      mat sample ( int N ) const;</span>
+<a name="l00357"></a>00357                         <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="l00358"></a>00358                         <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="l00360"></a><a class="code" href="classbdm_1_1egamma.html#0865cb3d6339fdc7410806cf70a329ed">00360</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="l00361"></a>00361                         vec&amp; _beta() {<span class="keywordflow">return</span> beta;}
+<a name="l00362"></a><a class="code" href="classbdm_1_1egamma.html#49d256c42cce14c6faa56ec242b57e85">00362</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="l00363"></a><a class="code" href="classbdm_1_1egamma.html#36986cc01917cd0796fadc17125bdec1">00363</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="l00364"></a>00364         };
 <a name="l00365"></a>00365 
-<a name="l00366"></a><a class="code" href="classbdm_1_1eigamma.html#3aff7bf25ddac27731c60826fcfd878f">00366</a>         vec <a class="code" href="classbdm_1_1eigamma.html#3aff7bf25ddac27731c60826fcfd878f" title="Returns a sample,  from density .">sample</a>()<span class="keyword"> const </span>{<span class="keywordflow">return</span> 1.0/<a class="code" href="classbdm_1_1eigamma.html#3aff7bf25ddac27731c60826fcfd878f" title="Returns a sample,  from density .">egamma::sample</a>();};
-<a name="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_1egamma.html#457bfb1ccb2057df85073e519a15ccc1" title="Vector .">beta</a>,<a class="code" href="classbdm_1_1egamma.html#0901ec983e66b8337aaa506e13b122fa" 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_1egamma.html#0901ec983e66b8337aaa506e13b122fa" title="Vector .">alpha</a>-2 );}
-<a name="l00370"></a>00370 };
-<a name="l00371"></a>00371 <span class="comment">/*</span>
-<a name="l00373"></a>00373 <span class="comment">class emix : public epdf {</span>
-<a name="l00374"></a>00374 <span class="comment">protected:</span>
-<a name="l00375"></a>00375 <span class="comment">        int n;</span>
-<a name="l00376"></a>00376 <span class="comment">        vec &amp;w;</span>
-<a name="l00377"></a>00377 <span class="comment">        Array&lt;epdf*&gt; Coms;</span>
-<a name="l00378"></a>00378 <span class="comment">public:</span>
-<a name="l00380"></a>00380 <span class="comment">        emix ( const RV &amp;rv, vec &amp;w0): epdf(rv), n(w0.length()), w(w0), Coms(n) {};</span>
-<a name="l00381"></a>00381 <span class="comment">        void set_parameters( int &amp;i, double wi, epdf* ep){w(i)=wi;Coms(i)=ep;}</span>
-<a name="l00382"></a>00382 <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="l00383"></a>00383 <span class="comment">        vec sample() {it_error ( "Not implemented" );return 0;}</span>
-<a name="l00384"></a>00384 <span class="comment">};</span>
-<a name="l00385"></a>00385 <span class="comment">*/</span>
-<a name="l00386"></a>00386 
-<a name="l00388"></a>00388 
-<a name="l00389"></a><a class="code" href="classbdm_1_1euni.html">00389</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="l00390"></a>00390 <span class="keyword">protected</span>:
-<a name="l00392"></a><a class="code" href="classbdm_1_1euni.html#ff7ce6a2ef5ef0015bbd1398bed12f32">00392</a>         vec <a class="code" href="classbdm_1_1euni.html#ff7ce6a2ef5ef0015bbd1398bed12f32" title="lower bound on support">low</a>;
-<a name="l00394"></a><a class="code" href="classbdm_1_1euni.html#cfad2dea4a62db6872bda8abd75f0de1">00394</a>         vec <a class="code" href="classbdm_1_1euni.html#cfad2dea4a62db6872bda8abd75f0de1" title="upper bound on support">high</a>;
-<a name="l00396"></a><a class="code" href="classbdm_1_1euni.html#d3c27e331f90c754d80228108de8ed4c">00396</a>         vec <a class="code" href="classbdm_1_1euni.html#d3c27e331f90c754d80228108de8ed4c" title="internal">distance</a>;
-<a name="l00398"></a><a class="code" href="classbdm_1_1euni.html#31bb13e8449a8eff35246d46dae35c20">00398</a>         <span class="keywordtype">double</span> <a class="code" href="classbdm_1_1euni.html#31bb13e8449a8eff35246d46dae35c20" title="normalizing coefficients">nk</a>;
-<a name="l00400"></a><a class="code" href="classbdm_1_1euni.html#3e63be48dd58659663ca60cd18700476">00400</a>         <span class="keywordtype">double</span> <a class="code" href="classbdm_1_1euni.html#3e63be48dd58659663ca60cd18700476" title="cache of log( nk )">lnk</a>;
-<a name="l00401"></a>00401 <span class="keyword">public</span>:
-<a name="l00404"></a>00404         <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="l00405"></a>00405         <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="l00406"></a>00406         <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="l00407"></a>00407                 <a class="code" href="classbdm_1_1euni.html#d3c27e331f90c754d80228108de8ed4c" title="internal">distance</a> = high0-low0;
-<a name="l00408"></a>00408                 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="l00409"></a>00409                 <a class="code" href="classbdm_1_1euni.html#ff7ce6a2ef5ef0015bbd1398bed12f32" title="lower bound on support">low</a> = low0;
-<a name="l00410"></a>00410                 <a class="code" href="classbdm_1_1euni.html#cfad2dea4a62db6872bda8abd75f0de1" title="upper bound on support">high</a> = high0;
-<a name="l00411"></a>00411                 <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="l00412"></a>00412                 <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="l00413"></a>00413                 <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="l00414"></a>00414         }
-<a name="l00416"></a>00416 
-<a name="l00417"></a>00417         <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="l00418"></a><a class="code" href="classbdm_1_1euni.html#caa07b8307bd793d5339d6583e0aba81">00418</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="l00419"></a><a class="code" href="classbdm_1_1euni.html#fc5df80359ead2918384b2004ce67194">00419</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="l00420"></a>00420                 vec smp ( <a class="code" href="classbdm_1_1epdf.html#16adac20ec7fe07e1ea0b27d917788ce" title="dimension of the random variable">dim</a> );
-<a name="l00421"></a>00421 <span class="preprocessor">#pragma omp critical</span>
-<a name="l00422"></a>00422 <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="l00423"></a>00423                 <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="l00424"></a>00424         }
-<a name="l00426"></a><a class="code" href="classbdm_1_1euni.html#46caa8c13aba2e6228f964208918b226">00426</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="l00427"></a><a class="code" href="classbdm_1_1euni.html#951f932155111f6053c980f672b4c22c">00427</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="l00428"></a>00428 };
-<a name="l00429"></a>00429 
-<a name="l00430"></a>00430 
-<a name="l00436"></a>00436 <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt;
-<a name="l00437"></a><a class="code" href="classbdm_1_1mlnorm.html">00437</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="l00438"></a>00438 <span class="keyword">protected</span>:
-<a name="l00440"></a><a class="code" href="classbdm_1_1mlnorm.html#150ad6acb223b0a0abeaf92346686dcd">00440</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="l00441"></a>00441         mat A;
-<a name="l00442"></a>00442         vec mu_const;
-<a name="l00443"></a>00443         vec&amp; _mu; <span class="comment">//cached epdf.mu;</span>
-<a name="l00444"></a>00444 <span class="keyword">public</span>:
-<a name="l00447"></a>00447         <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="l00448"></a>00448         <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="l00449"></a>00449                 <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="l00450"></a>00450         };
-<a name="l00452"></a>00452         <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="l00455"></a>00455         <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="l00456"></a>00456 
-<a name="l00458"></a><a class="code" href="classbdm_1_1mlnorm.html#56e587952f94fcac6cfc999eae6dbced">00458</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="l00460"></a><a class="code" href="classbdm_1_1mlnorm.html#262a2a486bff585f34bb6a5005b02614">00460</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="l00462"></a><a class="code" href="classbdm_1_1mlnorm.html#78120ecd1c2b1d7e80124b4603504604">00462</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="l00463"></a>00463 
-<a name="l00464"></a>00464         <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_M&gt;
-<a name="l00465"></a>00465         <span class="keyword">friend</span> std::ostream &amp;operator&lt;&lt; ( std::ostream &amp;os,  mlnorm&lt;sq_M&gt; &amp;ml );
-<a name="l00466"></a>00466 };
-<a name="l00467"></a>00467 
-<a name="l00469"></a>00469 <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt;
-<a name="l00470"></a><a class="code" href="classbdm_1_1mgnorm.html">00470</a> <span class="keyword">class </span><a class="code" href="classbdm_1_1mgnorm.html" title="Mpdf with general function for mean value.">mgnorm</a> : <span class="keyword">public</span> <a class="code" href="classbdm_1_1mEF.html" title="Exponential family model.">mEF</a> {
-<a name="l00471"></a>00471 <span class="keyword">protected</span>:
-<a name="l00473"></a><a class="code" href="classbdm_1_1mgnorm.html#8f7a376a1d2197e0634557e88e03104a">00473</a>         <a class="code" href="classbdm_1_1enorm.html" title="Gaussian density with positive definite (decomposed) covariance matrix.">enorm&lt;sq_T&gt;</a> <a class="code" href="classbdm_1_1epdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a>;
-<a name="l00474"></a>00474         vec &amp;mu;
-<a name="l00475"></a>00475         <a class="code" href="classbdm_1_1fnc.html" title="Class representing function  of variable  represented by rv.">fnc</a>* g;
-<a name="l00476"></a>00476 <span class="keyword">public</span>:
-<a name="l00478"></a><a class="code" href="classbdm_1_1mgnorm.html#1b014915d74470d3efab74e07cacb97d">00478</a>         <a class="code" href="classbdm_1_1mgnorm.html#1b014915d74470d3efab74e07cacb97d" title="default constructor">mgnorm</a>() :mu ( <a class="code" href="classbdm_1_1epdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a>._mu() ) {<a class="code" href="classbdm_1_1mpdf.html#5eea43c56d38e4441bfb30270db949c0" title="pointer to internal epdf">ep</a>=&amp;<a class="code" href="classbdm_1_1epdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a>;}
-<a name="l00480"></a><a class="code" href="classbdm_1_1mgnorm.html#578e02458e2a0d17f3864826b6ebd564">00480</a>         <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1mgnorm.html#578e02458e2a0d17f3864826b6ebd564" title="set mean function">set_parameters</a> ( <a class="code" href="classbdm_1_1fnc.html" title="Class representing function  of variable  represented by rv.">fnc</a>* g0, <span class="keyword">const</span> sq_T &amp;R0 ) {g=g0; <a class="code" href="classbdm_1_1epdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a>.set_parameters ( zeros ( g-&gt;<a class="code" href="classbdm_1_1fnc.html#083832294da9d1e40804158b979c4341" title="access function">dimension</a>() ), R0 );}
-<a name="l00481"></a><a class="code" href="classbdm_1_1mgnorm.html#b31d63472cf6a1030cd8dbd8094c1f6d">00481</a>         <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1mgnorm.html#b31d63472cf6a1030cd8dbd8094c1f6d" title="Update ep so that it represents this mpdf conditioned on rvc = cond.">condition</a> ( <span class="keyword">const</span> vec &amp;cond ) {mu=g-&gt;<a class="code" href="classbdm_1_1fnc.html#6277b11d7fffc7ef8a2fa3e84ae5bad4" title="function evaluates numerical value of  at  cond ">eval</a> ( cond );};
-<a name="l00482"></a>00482 };
-<a name="l00483"></a>00483 
-<a name="l00491"></a><a class="code" href="classbdm_1_1mlstudent.html">00491</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="l00492"></a>00492 <span class="keyword">protected</span>:
-<a name="l00493"></a>00493         <a class="code" href="classldmat.html" title="Matrix stored in LD form, (commonly known as UD).">ldmat</a> Lambda;
-<a name="l00494"></a>00494         <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="l00495"></a>00495         <a class="code" href="classldmat.html" title="Matrix stored in LD form, (commonly known as UD).">ldmat</a> Re;
-<a name="l00496"></a>00496 <span class="keyword">public</span>:
-<a name="l00497"></a>00497         <a class="code" href="classbdm_1_1mlstudent.html">mlstudent</a> ( ) :<a class="code" href="classbdm_1_1mlnorm.html">mlnorm&lt;ldmat&gt;</a> (),
-<a name="l00498"></a>00498                         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="l00499"></a>00499         <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="l00500"></a>00500                 it_assert_debug ( A0.rows() ==mu0.length(),<span class="stringliteral">""</span> );
-<a name="l00501"></a>00501                 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="l00502"></a>00502 
-<a name="l00503"></a>00503                 <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="l00504"></a>00504                 A = A0;
-<a name="l00505"></a>00505                 mu_const = mu0;
-<a name="l00506"></a>00506                 Re=R0;
-<a name="l00507"></a>00507                 Lambda = Lambda0;
-<a name="l00508"></a>00508         }
-<a name="l00509"></a><a class="code" href="classbdm_1_1mlstudent.html#efd37560585c8613897f30d3c2f58d0d">00509</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="l00510"></a>00510                 _mu = A*cond + mu_const;
-<a name="l00511"></a>00511                 <span class="keywordtype">double</span> zeta;
-<a name="l00512"></a>00512                 <span class="comment">//ugly hack!</span>
-<a name="l00513"></a>00513                 <span class="keywordflow">if</span> ( ( cond.length() +1 ) ==Lambda.<a class="code" href="group__math.html#g96dfb21865db4f5bd36fa70f9b0b1163" title="access function">rows</a>() ) {
-<a name="l00514"></a>00514                         zeta = Lambda.<a class="code" href="classldmat.html#d876c5f83e02b3e809b35c9de5068f14" title="Evaluates quadratic form ;.">invqform</a> ( concat ( cond, vec_1 ( 1.0 ) ) );
-<a name="l00515"></a>00515                 }
-<a name="l00516"></a>00516                 <span class="keywordflow">else</span> {
-<a name="l00517"></a>00517                         zeta = Lambda.<a class="code" href="classldmat.html#d876c5f83e02b3e809b35c9de5068f14" title="Evaluates quadratic form ;.">invqform</a> ( cond );
-<a name="l00518"></a>00518                 }
-<a name="l00519"></a>00519                 <a class="code" href="classbdm_1_1mlnorm.html#78120ecd1c2b1d7e80124b4603504604" title="access function">_R</a> = Re;
-<a name="l00520"></a>00520                 <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="l00521"></a>00521         };
-<a name="l00522"></a>00522 
-<a name="l00523"></a>00523 };
-<a name="l00533"></a><a class="code" href="classbdm_1_1mgamma.html">00533</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="l00534"></a>00534 <span class="keyword">protected</span>:
-<a name="l00536"></a><a class="code" href="classbdm_1_1mgamma.html#bdc9f1e9e03c09e91103fee269864438">00536</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="l00538"></a><a class="code" href="classbdm_1_1mgamma.html#b20cf88cca1fe9b0b8f2a412608bfd09">00538</a>         <span class="keywordtype">double</span> <a class="code" href="classbdm_1_1mgamma.html#b20cf88cca1fe9b0b8f2a412608bfd09" title="Constant .">k</a>;
-<a name="l00540"></a><a class="code" href="classbdm_1_1mgamma.html#3d95f4dde9214ff6dba265e18af60312">00540</a>         vec &amp;<a class="code" href="classbdm_1_1mgamma.html#3d95f4dde9214ff6dba265e18af60312" title="cache of epdf.beta">_beta</a>;
-<a name="l00541"></a>00541 
-<a name="l00542"></a>00542 <span class="keyword">public</span>:
-<a name="l00544"></a><a class="code" href="classbdm_1_1mgamma.html#1a9dc8661e5b214a8185d6e6b9956eb1">00544</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="l00546"></a>00546         <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="l00547"></a><a class="code" href="classbdm_1_1mgamma.html#8996500f1885e39cde30221b20900bff">00547</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="l00548"></a>00548 };
-<a name="l00549"></a>00549 
-<a name="l00559"></a><a class="code" href="classbdm_1_1migamma.html">00559</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="l00560"></a>00560 <span class="keyword">protected</span>:
-<a name="l00562"></a><a class="code" href="classbdm_1_1migamma.html#a31b39d4179551b593c9e0d7d756783a">00562</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="l00564"></a><a class="code" href="classbdm_1_1migamma.html#dc56bc9da542e0103ec16b9be8e5e38c">00564</a>         <span class="keywordtype">double</span> <a class="code" href="classbdm_1_1migamma.html#dc56bc9da542e0103ec16b9be8e5e38c" title="Constant .">k</a>;
-<a name="l00566"></a><a class="code" href="classbdm_1_1migamma.html#c9847093da59a9ba0ebb68d2c592f5dc">00566</a>         vec &amp;<a class="code" href="classbdm_1_1migamma.html#c9847093da59a9ba0ebb68d2c592f5dc" title="cache of epdf.alpha">_alpha</a>;
-<a name="l00568"></a><a class="code" href="classbdm_1_1migamma.html#0d854c047001b5465cf1ba21f52904b5">00568</a>         vec &amp;<a class="code" href="classbdm_1_1migamma.html#0d854c047001b5465cf1ba21f52904b5" title="cache of epdf.beta">_beta</a>;
-<a name="l00569"></a>00569 
-<a name="l00570"></a>00570 <span class="keyword">public</span>:
-<a name="l00573"></a>00573         <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="l00574"></a>00574         <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="l00576"></a>00576 
-<a name="l00578"></a><a class="code" href="classbdm_1_1migamma.html#8b10ab922e2a7bae2fb6bb3efc7b6151">00578</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="l00579"></a>00579                 <a class="code" href="classbdm_1_1migamma.html#dc56bc9da542e0103ec16b9be8e5e38c" title="Constant .">k</a>=k0;
-<a name="l00580"></a>00580                 <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="l00581"></a>00581                 <a class="code" href="classbdm_1_1mpdf.html#7c1900976ff13dbc09c9729b3bbff9e6" title="dimension of the condition">dimc</a> = dimension();
-<a name="l00582"></a>00582         };
-<a name="l00583"></a><a class="code" href="classbdm_1_1migamma.html#7a34b1e2e3aa2250d7c0ed7df1665b8c">00583</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="l00584"></a>00584                 <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="l00382"></a><a class="code" href="classbdm_1_1eigamma.html">00382</a>         <span class="keyword">class </span><a class="code" href="classbdm_1_1eigamma.html" title="Inverse-Gamma posterior density.">eigamma</a> : <span class="keyword">public</span> <a class="code" href="classbdm_1_1egamma.html" title="Gamma posterior density.">egamma</a>
+<a name="l00383"></a>00383         {
+<a name="l00384"></a>00384                 <span class="keyword">protected</span>:
+<a name="l00385"></a>00385                 <span class="keyword">public</span> :
+<a name="l00390"></a>00390 
+<a name="l00391"></a><a class="code" href="classbdm_1_1eigamma.html#3aff7bf25ddac27731c60826fcfd878f">00391</a>                         vec <a class="code" href="classbdm_1_1eigamma.html#3aff7bf25ddac27731c60826fcfd878f" title="Returns a sample,  from density .">sample</a>()<span class="keyword"> const </span>{<span class="keywordflow">return</span> 1.0/<a class="code" href="classbdm_1_1eigamma.html#3aff7bf25ddac27731c60826fcfd878f" title="Returns a sample,  from density .">egamma::sample</a>();};
+<a name="l00393"></a><a class="code" href="classbdm_1_1eigamma.html#46cecb295edbabd28120cb0f6f572bcb">00393</a>                         vec <a class="code" href="classbdm_1_1eigamma.html#46cecb295edbabd28120cb0f6f572bcb" title="Returns poiter to alpha and beta. Potentially dangerous: use with care!">mean</a>()<span class="keyword"> const </span>{<span class="keywordflow">return</span> elem_div ( <a class="code" href="classbdm_1_1egamma.html#457bfb1ccb2057df85073e519a15ccc1" title="Vector .">beta</a>,<a class="code" href="classbdm_1_1egamma.html#0901ec983e66b8337aaa506e13b122fa" title="Vector .">alpha</a>-1 );}
+<a name="l00394"></a><a class="code" href="classbdm_1_1eigamma.html#c2c696f8c668e9f65392c9449f6a5133">00394</a>                         vec <a class="code" href="classbdm_1_1eigamma.html#c2c696f8c668e9f65392c9449f6a5133" title="return expected variance (not covariance!)">variance</a>()<span class="keyword"> const </span>{vec mea=<a class="code" href="classbdm_1_1eigamma.html#46cecb295edbabd28120cb0f6f572bcb" title="Returns poiter to alpha and beta. Potentially dangerous: use with care!">mean</a>(); <span class="keywordflow">return</span> elem_div ( elem_mult ( mea,mea ),<a class="code" href="classbdm_1_1egamma.html#0901ec983e66b8337aaa506e13b122fa" title="Vector .">alpha</a>-2 );}
+<a name="l00395"></a>00395         };
+<a name="l00396"></a>00396         <span class="comment">/*</span>
+<a name="l00398"></a>00398 <span class="comment">        class emix : public epdf {</span>
+<a name="l00399"></a>00399 <span class="comment">        protected:</span>
+<a name="l00400"></a>00400 <span class="comment">                int n;</span>
+<a name="l00401"></a>00401 <span class="comment">                vec &amp;w;</span>
+<a name="l00402"></a>00402 <span class="comment">                Array&lt;epdf*&gt; Coms;</span>
+<a name="l00403"></a>00403 <span class="comment">        public:</span>
+<a name="l00405"></a>00405 <span class="comment">                emix ( const RV &amp;rv, vec &amp;w0): epdf(rv), n(w0.length()), w(w0), Coms(n) {};</span>
+<a name="l00406"></a>00406 <span class="comment">                void set_parameters( int &amp;i, double wi, epdf* ep){w(i)=wi;Coms(i)=ep;}</span>
+<a name="l00407"></a>00407 <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="l00408"></a>00408 <span class="comment">                vec sample() {it_error ( "Not implemented" );return 0;}</span>
+<a name="l00409"></a>00409 <span class="comment">        };</span>
+<a name="l00410"></a>00410 <span class="comment">        */</span>
+<a name="l00411"></a>00411 
+<a name="l00413"></a>00413 
+<a name="l00414"></a><a class="code" href="classbdm_1_1euni.html">00414</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="l00415"></a>00415         {
+<a name="l00416"></a>00416                 <span class="keyword">protected</span>:
+<a name="l00418"></a><a class="code" href="classbdm_1_1euni.html#ff7ce6a2ef5ef0015bbd1398bed12f32">00418</a>                         vec <a class="code" href="classbdm_1_1euni.html#ff7ce6a2ef5ef0015bbd1398bed12f32" title="lower bound on support">low</a>;
+<a name="l00420"></a><a class="code" href="classbdm_1_1euni.html#cfad2dea4a62db6872bda8abd75f0de1">00420</a>                         vec <a class="code" href="classbdm_1_1euni.html#cfad2dea4a62db6872bda8abd75f0de1" title="upper bound on support">high</a>;
+<a name="l00422"></a><a class="code" href="classbdm_1_1euni.html#d3c27e331f90c754d80228108de8ed4c">00422</a>                         vec <a class="code" href="classbdm_1_1euni.html#d3c27e331f90c754d80228108de8ed4c" title="internal">distance</a>;
+<a name="l00424"></a><a class="code" href="classbdm_1_1euni.html#31bb13e8449a8eff35246d46dae35c20">00424</a>                         <span class="keywordtype">double</span> <a class="code" href="classbdm_1_1euni.html#31bb13e8449a8eff35246d46dae35c20" title="normalizing coefficients">nk</a>;
+<a name="l00426"></a><a class="code" href="classbdm_1_1euni.html#3e63be48dd58659663ca60cd18700476">00426</a>                         <span class="keywordtype">double</span> <a class="code" href="classbdm_1_1euni.html#3e63be48dd58659663ca60cd18700476" title="cache of log( nk )">lnk</a>;
+<a name="l00427"></a>00427                 <span class="keyword">public</span>:
+<a name="l00430"></a>00430                         <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="l00431"></a>00431                         <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="l00432"></a>00432                         <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="l00433"></a>00433                         {
+<a name="l00434"></a>00434                                 <a class="code" href="classbdm_1_1euni.html#d3c27e331f90c754d80228108de8ed4c" title="internal">distance</a> = high0-low0;
+<a name="l00435"></a>00435                                 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="l00436"></a>00436                                 <a class="code" href="classbdm_1_1euni.html#ff7ce6a2ef5ef0015bbd1398bed12f32" title="lower bound on support">low</a> = low0;
+<a name="l00437"></a>00437                                 <a class="code" href="classbdm_1_1euni.html#cfad2dea4a62db6872bda8abd75f0de1" title="upper bound on support">high</a> = high0;
+<a name="l00438"></a>00438                                 <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="l00439"></a>00439                                 <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="l00440"></a>00440                                 <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="l00441"></a>00441                         }
+<a name="l00443"></a>00443 
+<a name="l00444"></a>00444                         <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="l00445"></a><a class="code" href="classbdm_1_1euni.html#caa07b8307bd793d5339d6583e0aba81">00445</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="l00446"></a><a class="code" href="classbdm_1_1euni.html#fc5df80359ead2918384b2004ce67194">00446</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="l00447"></a>00447 <span class="keyword">                        </span>{
+<a name="l00448"></a>00448                                 vec smp ( <a class="code" href="classbdm_1_1epdf.html#16adac20ec7fe07e1ea0b27d917788ce" title="dimension of the random variable">dim</a> );
+<a name="l00449"></a>00449 <span class="preprocessor">#pragma omp critical</span>
+<a name="l00450"></a>00450 <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="l00451"></a>00451                                 <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="l00452"></a>00452                         }
+<a name="l00454"></a><a class="code" href="classbdm_1_1euni.html#46caa8c13aba2e6228f964208918b226">00454</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="l00455"></a><a class="code" href="classbdm_1_1euni.html#951f932155111f6053c980f672b4c22c">00455</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="l00456"></a>00456         };
+<a name="l00457"></a>00457 
+<a name="l00458"></a>00458 
+<a name="l00464"></a>00464         <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt;
+<a name="l00465"></a><a class="code" href="classbdm_1_1mlnorm.html">00465</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="l00466"></a>00466         {
+<a name="l00467"></a>00467                 <span class="keyword">protected</span>:
+<a name="l00469"></a><a class="code" href="classbdm_1_1mlnorm.html#150ad6acb223b0a0abeaf92346686dcd">00469</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="l00470"></a>00470                         mat A;
+<a name="l00471"></a>00471                         vec mu_const;
+<a name="l00472"></a>00472                         vec&amp; _mu; <span class="comment">//cached epdf.mu;</span>
+<a name="l00473"></a>00473                 <span class="keyword">public</span>:
+<a name="l00476"></a>00476                         <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="l00477"></a>00477                         <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="l00478"></a>00478                         {
+<a name="l00479"></a>00479                                 <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="l00480"></a>00480                         };
+<a name="l00482"></a>00482                         <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="l00485"></a>00485                         <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="l00486"></a>00486 
+<a name="l00488"></a><a class="code" href="classbdm_1_1mlnorm.html#56e587952f94fcac6cfc999eae6dbced">00488</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="l00490"></a><a class="code" href="classbdm_1_1mlnorm.html#262a2a486bff585f34bb6a5005b02614">00490</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="l00492"></a><a class="code" href="classbdm_1_1mlnorm.html#78120ecd1c2b1d7e80124b4603504604">00492</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="l00493"></a>00493 
+<a name="l00494"></a>00494                         <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_M&gt;
+<a name="l00495"></a>00495                         <span class="keyword">friend</span> std::ostream &amp;operator&lt;&lt; ( std::ostream &amp;os,  mlnorm&lt;sq_M&gt; &amp;ml );
+<a name="l00496"></a>00496         };
+<a name="l00497"></a>00497 
+<a name="l00499"></a>00499         <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt;
+<a name="l00500"></a><a class="code" href="classbdm_1_1mgnorm.html">00500</a>         <span class="keyword">class </span><a class="code" href="classbdm_1_1mgnorm.html" title="Mpdf with general function for mean value.">mgnorm</a> : <span class="keyword">public</span> <a class="code" href="classbdm_1_1mEF.html" title="Exponential family model.">mEF</a>
+<a name="l00501"></a>00501         {
+<a name="l00502"></a>00502                 <span class="keyword">protected</span>:
+<a name="l00504"></a><a class="code" href="classbdm_1_1mgnorm.html#8f7a376a1d2197e0634557e88e03104a">00504</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="l00505"></a>00505                         vec &amp;mu;
+<a name="l00506"></a>00506                         <a class="code" href="classbdm_1_1fnc.html" title="Class representing function  of variable  represented by rv.">fnc</a>* g;
+<a name="l00507"></a>00507                 <span class="keyword">public</span>:
+<a name="l00509"></a><a class="code" href="classbdm_1_1mgnorm.html#1b014915d74470d3efab74e07cacb97d">00509</a>                         <a class="code" href="classbdm_1_1mgnorm.html#1b014915d74470d3efab74e07cacb97d" title="default constructor">mgnorm</a>() :mu ( <a class="code" href="classbdm_1_1epdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a>._mu() ) {<a class="code" href="classbdm_1_1mpdf.html#5eea43c56d38e4441bfb30270db949c0" title="pointer to internal epdf">ep</a>=&amp;<a class="code" href="classbdm_1_1epdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a>;}
+<a name="l00511"></a><a class="code" href="classbdm_1_1mgnorm.html#578e02458e2a0d17f3864826b6ebd564">00511</a>                         <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1mgnorm.html#578e02458e2a0d17f3864826b6ebd564" title="set mean function">set_parameters</a> ( <a class="code" href="classbdm_1_1fnc.html" title="Class representing function  of variable  represented by rv.">fnc</a>* g0, <span class="keyword">const</span> sq_T &amp;R0 ) {g=g0; <a class="code" href="classbdm_1_1epdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a>.set_parameters ( zeros ( g-&gt;<a class="code" href="classbdm_1_1fnc.html#083832294da9d1e40804158b979c4341" title="access function">dimension</a>() ), R0 );}
+<a name="l00512"></a><a class="code" href="classbdm_1_1mgnorm.html#b31d63472cf6a1030cd8dbd8094c1f6d">00512</a>                         <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1mgnorm.html#b31d63472cf6a1030cd8dbd8094c1f6d" title="Update ep so that it represents this mpdf conditioned on rvc = cond.">condition</a> ( <span class="keyword">const</span> vec &amp;cond ) {mu=g-&gt;<a class="code" href="classbdm_1_1fnc.html#6277b11d7fffc7ef8a2fa3e84ae5bad4" title="function evaluates numerical value of  at  cond ">eval</a> ( cond );};
+<a name="l00513"></a>00513         };
+<a name="l00514"></a>00514 
+<a name="l00522"></a><a class="code" href="classbdm_1_1mlstudent.html">00522</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="l00523"></a>00523         {
+<a name="l00524"></a>00524                 <span class="keyword">protected</span>:
+<a name="l00525"></a>00525                         <a class="code" href="classldmat.html" title="Matrix stored in LD form, (commonly known as UD).">ldmat</a> Lambda;
+<a name="l00526"></a>00526                         <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="l00527"></a>00527                         <a class="code" href="classldmat.html" title="Matrix stored in LD form, (commonly known as UD).">ldmat</a> Re;
+<a name="l00528"></a>00528                 <span class="keyword">public</span>:
+<a name="l00529"></a>00529                         <a class="code" href="classbdm_1_1mlstudent.html">mlstudent</a> ( ) :<a class="code" href="classbdm_1_1mlnorm.html">mlnorm&lt;ldmat&gt;</a> (),
+<a name="l00530"></a>00530                                         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="l00531"></a>00531                         <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="l00532"></a>00532                         {
+<a name="l00533"></a>00533                                 it_assert_debug ( A0.rows() ==mu0.length(),<span class="stringliteral">""</span> );
+<a name="l00534"></a>00534                                 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="l00535"></a>00535 
+<a name="l00536"></a>00536                                 <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="l00537"></a>00537                                 A = A0;
+<a name="l00538"></a>00538                                 mu_const = mu0;
+<a name="l00539"></a>00539                                 Re=R0;
+<a name="l00540"></a>00540                                 Lambda = Lambda0;
+<a name="l00541"></a>00541                         }
+<a name="l00542"></a><a class="code" href="classbdm_1_1mlstudent.html#efd37560585c8613897f30d3c2f58d0d">00542</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="l00543"></a>00543                         {
+<a name="l00544"></a>00544                                 _mu = A*cond + mu_const;
+<a name="l00545"></a>00545                                 <span class="keywordtype">double</span> zeta;
+<a name="l00546"></a>00546                                 <span class="comment">//ugly hack!</span>
+<a name="l00547"></a>00547                                 <span class="keywordflow">if</span> ( ( cond.length() +1 ) ==Lambda.<a class="code" href="group__math.html#g96dfb21865db4f5bd36fa70f9b0b1163" title="access function">rows</a>() )
+<a name="l00548"></a>00548                                 {
+<a name="l00549"></a>00549                                         zeta = Lambda.<a class="code" href="classldmat.html#d876c5f83e02b3e809b35c9de5068f14" title="Evaluates quadratic form ;.">invqform</a> ( concat ( cond, vec_1 ( 1.0 ) ) );
+<a name="l00550"></a>00550                                 }
+<a name="l00551"></a>00551                                 <span class="keywordflow">else</span>
+<a name="l00552"></a>00552                                 {
+<a name="l00553"></a>00553                                         zeta = Lambda.<a class="code" href="classldmat.html#d876c5f83e02b3e809b35c9de5068f14" title="Evaluates quadratic form ;.">invqform</a> ( cond );
+<a name="l00554"></a>00554                                 }
+<a name="l00555"></a>00555                                 <a class="code" href="classbdm_1_1mlnorm.html#78120ecd1c2b1d7e80124b4603504604" title="access function">_R</a> = Re;
+<a name="l00556"></a>00556                                 <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="l00557"></a>00557                         };
+<a name="l00558"></a>00558 
+<a name="l00559"></a>00559         };
+<a name="l00569"></a><a class="code" href="classbdm_1_1mgamma.html">00569</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="l00570"></a>00570         {
+<a name="l00571"></a>00571                 <span class="keyword">protected</span>:
+<a name="l00573"></a><a class="code" href="classbdm_1_1mgamma.html#bdc9f1e9e03c09e91103fee269864438">00573</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="l00575"></a><a class="code" href="classbdm_1_1mgamma.html#b20cf88cca1fe9b0b8f2a412608bfd09">00575</a>                         <span class="keywordtype">double</span> <a class="code" href="classbdm_1_1mgamma.html#b20cf88cca1fe9b0b8f2a412608bfd09" title="Constant .">k</a>;
+<a name="l00577"></a><a class="code" href="classbdm_1_1mgamma.html#3d95f4dde9214ff6dba265e18af60312">00577</a>                         vec &amp;<a class="code" href="classbdm_1_1mgamma.html#3d95f4dde9214ff6dba265e18af60312" title="cache of epdf.beta">_beta</a>;
+<a name="l00578"></a>00578 
+<a name="l00579"></a>00579                 <span class="keyword">public</span>:
+<a name="l00581"></a><a class="code" href="classbdm_1_1mgamma.html#1a9dc8661e5b214a8185d6e6b9956eb1">00581</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="l00583"></a>00583                         <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="l00584"></a><a class="code" href="classbdm_1_1mgamma.html#8996500f1885e39cde30221b20900bff">00584</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="l00585"></a>00585         };
-<a name="l00586"></a>00586 };
-<a name="l00587"></a>00587 
-<a name="l00599"></a><a class="code" href="classbdm_1_1mgamma__fix.html">00599</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="l00600"></a>00600 <span class="keyword">protected</span>:
-<a name="l00602"></a><a class="code" href="classbdm_1_1mgamma__fix.html#1eb701506aabb2e6af007e487212d6fa">00602</a>         <span class="keywordtype">double</span> <a class="code" href="classbdm_1_1mgamma__fix.html#1eb701506aabb2e6af007e487212d6fa" title="parameter l">l</a>;
-<a name="l00604"></a><a class="code" href="classbdm_1_1mgamma__fix.html#018c6f901a04e419455308a07eb3b0b2">00604</a>         vec <a class="code" href="classbdm_1_1mgamma__fix.html#018c6f901a04e419455308a07eb3b0b2" title="reference vector">refl</a>;
-<a name="l00605"></a>00605 <span class="keyword">public</span>:
-<a name="l00607"></a><a class="code" href="classbdm_1_1mgamma__fix.html#9a31bc9b4b60188a18a2a6b588dc4b2d">00607</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="l00609"></a><a class="code" href="classbdm_1_1mgamma__fix.html#1bfd30e90db9dc1fbda4a9fbb0b716b2">00609</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="l00610"></a>00610                 <a class="code" href="classbdm_1_1mgamma__fix.html#1bfd30e90db9dc1fbda4a9fbb0b716b2" title="Set value of k.">mgamma::set_parameters</a> ( k0, ref0 );
-<a name="l00611"></a>00611                 <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="l00612"></a>00612                 <a class="code" href="classbdm_1_1mpdf.html#7c1900976ff13dbc09c9729b3bbff9e6" title="dimension of the condition">dimc</a>=dimension();
-<a name="l00613"></a>00613         };
+<a name="l00586"></a>00586 
+<a name="l00596"></a><a class="code" href="classbdm_1_1migamma.html">00596</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="l00597"></a>00597         {
+<a name="l00598"></a>00598                 <span class="keyword">protected</span>:
+<a name="l00600"></a><a class="code" href="classbdm_1_1migamma.html#a31b39d4179551b593c9e0d7d756783a">00600</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="l00602"></a><a class="code" href="classbdm_1_1migamma.html#dc56bc9da542e0103ec16b9be8e5e38c">00602</a>                         <span class="keywordtype">double</span> <a class="code" href="classbdm_1_1migamma.html#dc56bc9da542e0103ec16b9be8e5e38c" title="Constant .">k</a>;
+<a name="l00604"></a><a class="code" href="classbdm_1_1migamma.html#c9847093da59a9ba0ebb68d2c592f5dc">00604</a>                         vec &amp;<a class="code" href="classbdm_1_1migamma.html#c9847093da59a9ba0ebb68d2c592f5dc" title="cache of epdf.alpha">_alpha</a>;
+<a name="l00606"></a><a class="code" href="classbdm_1_1migamma.html#0d854c047001b5465cf1ba21f52904b5">00606</a>                         vec &amp;<a class="code" href="classbdm_1_1migamma.html#0d854c047001b5465cf1ba21f52904b5" title="cache of epdf.beta">_beta</a>;
+<a name="l00607"></a>00607 
+<a name="l00608"></a>00608                 <span class="keyword">public</span>:
+<a name="l00611"></a>00611                         <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="l00612"></a>00612                         <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="l00614"></a>00614 
-<a name="l00615"></a><a class="code" href="classbdm_1_1mgamma__fix.html#1d539591deb7a38bb3403c2b396c8ff7">00615</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="l00616"></a>00616 };
-<a name="l00617"></a>00617 
-<a name="l00618"></a>00618 
-<a name="l00631"></a><a class="code" href="classbdm_1_1migamma__ref.html">00631</a> <span class="keyword">class </span><a class="code" href="classbdm_1_1migamma__ref.html" title="Inverse-Gamma random walk around a fixed point.">migamma_ref</a> : <span class="keyword">public</span> <a class="code" href="classbdm_1_1migamma.html" title="Inverse-Gamma random walk.">migamma</a> {
-<a name="l00632"></a>00632 <span class="keyword">protected</span>:
-<a name="l00634"></a><a class="code" href="classbdm_1_1migamma__ref.html#cdc1345ba8375fbdb18a69322d2f841d">00634</a>         <span class="keywordtype">double</span> <a class="code" href="classbdm_1_1migamma__ref.html#cdc1345ba8375fbdb18a69322d2f841d" title="parameter l">l</a>;
-<a name="l00636"></a><a class="code" href="classbdm_1_1migamma__ref.html#3692dc67caf4367e15564d37f45476f6">00636</a>         vec <a class="code" href="classbdm_1_1migamma__ref.html#3692dc67caf4367e15564d37f45476f6" title="reference vector">refl</a>;
-<a name="l00637"></a>00637 <span class="keyword">public</span>:
-<a name="l00639"></a><a class="code" href="classbdm_1_1migamma__ref.html#f45b15a10f084991ba6b48295f10421f">00639</a>         <a class="code" href="classbdm_1_1migamma__ref.html#f45b15a10f084991ba6b48295f10421f" title="Constructor.">migamma_ref</a> ( ) : <a class="code" href="classbdm_1_1migamma.html" title="Inverse-Gamma random walk.">migamma</a> (),<a class="code" href="classbdm_1_1migamma__ref.html#3692dc67caf4367e15564d37f45476f6" title="reference vector">refl</a> ( ) {};
-<a name="l00641"></a><a class="code" href="classbdm_1_1migamma__ref.html#b0b4eb278ef5d0831ec4954ba7bd2800">00641</a>         <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1migamma__ref.html#b0b4eb278ef5d0831ec4954ba7bd2800" title="Set value of k.">set_parameters</a> ( <span class="keywordtype">double</span> k0 , vec ref0, <span class="keywordtype">double</span> l0 ) {
-<a name="l00642"></a>00642                 <a class="code" href="classbdm_1_1migamma__ref.html#b0b4eb278ef5d0831ec4954ba7bd2800" title="Set value of k.">migamma::set_parameters</a> ( ref0.length(), k0 );
-<a name="l00643"></a>00643                 <a class="code" href="classbdm_1_1migamma__ref.html#3692dc67caf4367e15564d37f45476f6" title="reference vector">refl</a>=pow ( ref0,1.0-l0 );
-<a name="l00644"></a>00644                 <a class="code" href="classbdm_1_1migamma__ref.html#cdc1345ba8375fbdb18a69322d2f841d" title="parameter l">l</a>=l0;
-<a name="l00645"></a>00645                 <a class="code" href="classbdm_1_1mpdf.html#7c1900976ff13dbc09c9729b3bbff9e6" title="dimension of the condition">dimc</a> = dimension();
-<a name="l00646"></a>00646         };
-<a name="l00647"></a>00647 
-<a name="l00648"></a><a class="code" href="classbdm_1_1migamma__ref.html#ae86b2e4ff963d62e05d4e130514634a">00648</a>         <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1migamma__ref.html#ae86b2e4ff963d62e05d4e130514634a" title="Update ep so that it represents this mpdf conditioned on rvc = cond.">condition</a> ( <span class="keyword">const</span> vec &amp;val ) {
-<a name="l00649"></a>00649                 vec mean=elem_mult ( <a class="code" href="classbdm_1_1migamma__ref.html#3692dc67caf4367e15564d37f45476f6" title="reference vector">refl</a>,pow ( val,<a class="code" href="classbdm_1_1migamma__ref.html#cdc1345ba8375fbdb18a69322d2f841d" title="parameter l">l</a> ) );
-<a name="l00650"></a>00650                 <a class="code" href="classbdm_1_1migamma__ref.html#ae86b2e4ff963d62e05d4e130514634a" title="Update ep so that it represents this mpdf conditioned on rvc = cond.">migamma::condition</a> ( mean );
-<a name="l00651"></a>00651         };
-<a name="l00652"></a>00652 };
-<a name="l00653"></a>00653 
-<a name="l00663"></a><a class="code" href="classbdm_1_1elognorm.html">00663</a> <span class="keyword">class </span><a class="code" href="classbdm_1_1elognorm.html">elognorm</a>: <span class="keyword">public</span> <a class="code" href="classbdm_1_1enorm.html" title="Gaussian density with positive definite (decomposed) covariance matrix.">enorm</a>&lt;ldmat&gt;{
-<a name="l00664"></a>00664         <span class="keyword">public</span>:
-<a name="l00665"></a><a class="code" href="classbdm_1_1elognorm.html#8b948e2bce1253765a2542199913aaba">00665</a>                 vec <a class="code" href="classbdm_1_1elognorm.html#8b948e2bce1253765a2542199913aaba" title="Returns a sample,  from density .">sample</a>()<span class="keyword"> const </span>{<span class="keywordflow">return</span> exp(<a class="code" href="classbdm_1_1enorm.html" title="Gaussian density with positive definite (decomposed) covariance matrix.">enorm&lt;ldmat&gt;::sample</a>());};
-<a name="l00666"></a><a class="code" href="classbdm_1_1elognorm.html#adb41e4f4d6600dec6f8c1dbc5ed9eea">00666</a>                 vec <a class="code" href="classbdm_1_1elognorm.html#adb41e4f4d6600dec6f8c1dbc5ed9eea" title="return expected value">mean</a>()<span class="keyword"> const </span>{vec var=<a class="code" href="classbdm_1_1enorm.html#729c75ef0fa8abae03d58ad1f81e6773" title="return expected variance (not covariance!)">enorm&lt;ldmat&gt;::variance</a>();<span class="keywordflow">return</span> exp(<a class="code" href="classbdm_1_1enorm.html#c702a194720853570d08b65482f842c7" title="mean value">mu</a> - 0.5*var);};
-<a name="l00667"></a>00667                 
-<a name="l00668"></a>00668 };
-<a name="l00669"></a>00669 
-<a name="l00681"></a><a class="code" href="classbdm_1_1mlognorm.html">00681</a> <span class="keyword">class </span><a class="code" href="classbdm_1_1mlognorm.html" title="Log-Normal random walk.">mlognorm</a> : <span class="keyword">public</span> <a class="code" href="classbdm_1_1mpdf.html" title="Conditional probability density, e.g. modeling some dependencies.">mpdf</a> {
-<a name="l00682"></a>00682         <span class="keyword">protected</span>:
-<a name="l00683"></a>00683                 <a class="code" href="classbdm_1_1elognorm.html">elognorm</a> eno;
-<a name="l00685"></a><a class="code" href="classbdm_1_1mlognorm.html#a51128a2e503b8b2ce698244b9e0db1a">00685</a>                 <span class="keywordtype">double</span> <a class="code" href="classbdm_1_1mlognorm.html#a51128a2e503b8b2ce698244b9e0db1a" title="parameter 1/2*sigma^2">sig2</a>;
-<a name="l00687"></a><a class="code" href="classbdm_1_1mlognorm.html#7d0063f77d899ef22e8c5edd642176d2">00687</a>                 vec &amp;<a class="code" href="classbdm_1_1mlognorm.html#7d0063f77d899ef22e8c5edd642176d2" title="access">mu</a>;
-<a name="l00688"></a>00688         <span class="keyword">public</span>:
-<a name="l00690"></a><a class="code" href="classbdm_1_1mlognorm.html#a5d6eb2688d02e0348b96c4fbd7bde41">00690</a>                 <a class="code" href="classbdm_1_1mlognorm.html#a5d6eb2688d02e0348b96c4fbd7bde41" title="Constructor.">mlognorm</a> ( ) : eno (), <a class="code" href="classbdm_1_1mlognorm.html#7d0063f77d899ef22e8c5edd642176d2" title="access">mu</a>(eno._mu()) {<a class="code" href="classbdm_1_1mpdf.html#5eea43c56d38e4441bfb30270db949c0" title="pointer to internal epdf">ep</a>=&amp;eno;};
-<a name="l00692"></a><a class="code" href="classbdm_1_1mlognorm.html#604cab0e8a76f9041dc3c606043bb39f">00692</a>                 <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1mlognorm.html#604cab0e8a76f9041dc3c606043bb39f" title="Set value of k.">set_parameters</a> ( <span class="keywordtype">int</span> size, <span class="keywordtype">double</span> k) {
-<a name="l00693"></a>00693                         <a class="code" href="classbdm_1_1mlognorm.html#a51128a2e503b8b2ce698244b9e0db1a" title="parameter 1/2*sigma^2">sig2</a> = 0.5*log(k*k+1);
-<a name="l00694"></a>00694                         eno.<a class="code" href="classbdm_1_1enorm.html#b8322f2c11560871dd922c660f4771bb">set_parameters</a>(zeros(size),2*<a class="code" href="classbdm_1_1mlognorm.html#a51128a2e503b8b2ce698244b9e0db1a" title="parameter 1/2*sigma^2">sig2</a>*eye(size));
-<a name="l00695"></a>00695                         
-<a name="l00696"></a>00696                         <a class="code" href="classbdm_1_1mpdf.html#7c1900976ff13dbc09c9729b3bbff9e6" title="dimension of the condition">dimc</a> = size;
-<a name="l00697"></a>00697                 };
+<a name="l00616"></a><a class="code" href="classbdm_1_1migamma.html#8b10ab922e2a7bae2fb6bb3efc7b6151">00616</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="l00617"></a>00617                         {
+<a name="l00618"></a>00618                                 <a class="code" href="classbdm_1_1migamma.html#dc56bc9da542e0103ec16b9be8e5e38c" title="Constant .">k</a>=k0;
+<a name="l00619"></a>00619                                 <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="l00620"></a>00620                                 <a class="code" href="classbdm_1_1mpdf.html#7c1900976ff13dbc09c9729b3bbff9e6" title="dimension of the condition">dimc</a> = dimension();
+<a name="l00621"></a>00621                         };
+<a name="l00622"></a><a class="code" href="classbdm_1_1migamma.html#7a34b1e2e3aa2250d7c0ed7df1665b8c">00622</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="l00623"></a>00623                         {
+<a name="l00624"></a>00624                                 <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="l00625"></a>00625                         };
+<a name="l00626"></a>00626         };
+<a name="l00627"></a>00627 
+<a name="l00639"></a><a class="code" href="classbdm_1_1mgamma__fix.html">00639</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="l00640"></a>00640         {
+<a name="l00641"></a>00641                 <span class="keyword">protected</span>:
+<a name="l00643"></a><a class="code" href="classbdm_1_1mgamma__fix.html#1eb701506aabb2e6af007e487212d6fa">00643</a>                         <span class="keywordtype">double</span> <a class="code" href="classbdm_1_1mgamma__fix.html#1eb701506aabb2e6af007e487212d6fa" title="parameter l">l</a>;
+<a name="l00645"></a><a class="code" href="classbdm_1_1mgamma__fix.html#018c6f901a04e419455308a07eb3b0b2">00645</a>                         vec <a class="code" href="classbdm_1_1mgamma__fix.html#018c6f901a04e419455308a07eb3b0b2" title="reference vector">refl</a>;
+<a name="l00646"></a>00646                 <span class="keyword">public</span>:
+<a name="l00648"></a><a class="code" href="classbdm_1_1mgamma__fix.html#9a31bc9b4b60188a18a2a6b588dc4b2d">00648</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="l00650"></a><a class="code" href="classbdm_1_1mgamma__fix.html#1bfd30e90db9dc1fbda4a9fbb0b716b2">00650</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="l00651"></a>00651                         {
+<a name="l00652"></a>00652                                 <a class="code" href="classbdm_1_1mgamma__fix.html#1bfd30e90db9dc1fbda4a9fbb0b716b2" title="Set value of k.">mgamma::set_parameters</a> ( k0, ref0 );
+<a name="l00653"></a>00653                                 <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="l00654"></a>00654                                 <a class="code" href="classbdm_1_1mpdf.html#7c1900976ff13dbc09c9729b3bbff9e6" title="dimension of the condition">dimc</a>=dimension();
+<a name="l00655"></a>00655                         };
+<a name="l00656"></a>00656 
+<a name="l00657"></a><a class="code" href="classbdm_1_1mgamma__fix.html#1d539591deb7a38bb3403c2b396c8ff7">00657</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="l00658"></a>00658         };
+<a name="l00659"></a>00659 
+<a name="l00660"></a>00660 
+<a name="l00673"></a><a class="code" href="classbdm_1_1migamma__ref.html">00673</a>         <span class="keyword">class </span><a class="code" href="classbdm_1_1migamma__ref.html" title="Inverse-Gamma random walk around a fixed point.">migamma_ref</a> : <span class="keyword">public</span> <a class="code" href="classbdm_1_1migamma.html" title="Inverse-Gamma random walk.">migamma</a>
+<a name="l00674"></a>00674         {
+<a name="l00675"></a>00675                 <span class="keyword">protected</span>:
+<a name="l00677"></a><a class="code" href="classbdm_1_1migamma__ref.html#cdc1345ba8375fbdb18a69322d2f841d">00677</a>                         <span class="keywordtype">double</span> <a class="code" href="classbdm_1_1migamma__ref.html#cdc1345ba8375fbdb18a69322d2f841d" title="parameter l">l</a>;
+<a name="l00679"></a><a class="code" href="classbdm_1_1migamma__ref.html#3692dc67caf4367e15564d37f45476f6">00679</a>                         vec <a class="code" href="classbdm_1_1migamma__ref.html#3692dc67caf4367e15564d37f45476f6" title="reference vector">refl</a>;
+<a name="l00680"></a>00680                 <span class="keyword">public</span>:
+<a name="l00682"></a><a class="code" href="classbdm_1_1migamma__ref.html#f45b15a10f084991ba6b48295f10421f">00682</a>                         <a class="code" href="classbdm_1_1migamma__ref.html#f45b15a10f084991ba6b48295f10421f" title="Constructor.">migamma_ref</a> ( ) : <a class="code" href="classbdm_1_1migamma.html" title="Inverse-Gamma random walk.">migamma</a> (),<a class="code" href="classbdm_1_1migamma__ref.html#3692dc67caf4367e15564d37f45476f6" title="reference vector">refl</a> ( ) {};
+<a name="l00684"></a><a class="code" href="classbdm_1_1migamma__ref.html#b0b4eb278ef5d0831ec4954ba7bd2800">00684</a>                         <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1migamma__ref.html#b0b4eb278ef5d0831ec4954ba7bd2800" title="Set value of k.">set_parameters</a> ( <span class="keywordtype">double</span> k0 , vec ref0, <span class="keywordtype">double</span> l0 )
+<a name="l00685"></a>00685                         {
+<a name="l00686"></a>00686                                 <a class="code" href="classbdm_1_1migamma__ref.html#b0b4eb278ef5d0831ec4954ba7bd2800" title="Set value of k.">migamma::set_parameters</a> ( ref0.length(), k0 );
+<a name="l00687"></a>00687                                 <a class="code" href="classbdm_1_1migamma__ref.html#3692dc67caf4367e15564d37f45476f6" title="reference vector">refl</a>=pow ( ref0,1.0-l0 );
+<a name="l00688"></a>00688                                 <a class="code" href="classbdm_1_1migamma__ref.html#cdc1345ba8375fbdb18a69322d2f841d" title="parameter l">l</a>=l0;
+<a name="l00689"></a>00689                                 <a class="code" href="classbdm_1_1mpdf.html#7c1900976ff13dbc09c9729b3bbff9e6" title="dimension of the condition">dimc</a> = dimension();
+<a name="l00690"></a>00690                         };
+<a name="l00691"></a>00691 
+<a name="l00692"></a><a class="code" href="classbdm_1_1migamma__ref.html#ae86b2e4ff963d62e05d4e130514634a">00692</a>                         <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1migamma__ref.html#ae86b2e4ff963d62e05d4e130514634a" title="Update ep so that it represents this mpdf conditioned on rvc = cond.">condition</a> ( <span class="keyword">const</span> vec &amp;val )
+<a name="l00693"></a>00693                         {
+<a name="l00694"></a>00694                                 vec mean=elem_mult ( <a class="code" href="classbdm_1_1migamma__ref.html#3692dc67caf4367e15564d37f45476f6" title="reference vector">refl</a>,pow ( val,<a class="code" href="classbdm_1_1migamma__ref.html#cdc1345ba8375fbdb18a69322d2f841d" title="parameter l">l</a> ) );
+<a name="l00695"></a>00695                                 <a class="code" href="classbdm_1_1migamma__ref.html#ae86b2e4ff963d62e05d4e130514634a" title="Update ep so that it represents this mpdf conditioned on rvc = cond.">migamma::condition</a> ( mean );
+<a name="l00696"></a>00696                         };
+<a name="l00697"></a>00697         };
 <a name="l00698"></a>00698 
-<a name="l00699"></a><a class="code" href="classbdm_1_1mlognorm.html#9106d8fd8bdf2b6be675ffd8f3ca584e">00699</a>                 <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1mlognorm.html#9106d8fd8bdf2b6be675ffd8f3ca584e" title="Update ep so that it represents this mpdf conditioned on rvc = cond.">condition</a> ( <span class="keyword">const</span> vec &amp;val ) {
-<a name="l00700"></a>00700                         <a class="code" href="classbdm_1_1mlognorm.html#7d0063f77d899ef22e8c5edd642176d2" title="access">mu</a>=log(val)-<a class="code" href="classbdm_1_1mlognorm.html#a51128a2e503b8b2ce698244b9e0db1a" title="parameter 1/2*sigma^2">sig2</a>;<span class="comment">//elem_mult ( refl,pow ( val,l ) );</span>
-<a name="l00701"></a>00701                 };
-<a name="l00702"></a>00702 };
-<a name="l00703"></a>00703 
-<a name="l00707"></a><a class="code" href="classbdm_1_1iW.html">00707</a> <span class="keyword">class </span><a class="code" href="classbdm_1_1iW.html">iW</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="l00708"></a>00708         <span class="keyword">public</span>:
-<a name="l00709"></a>00709                 vec sample(){<span class="keywordflow">return</span> vec();}
-<a name="l00710"></a>00710 };
-<a name="l00711"></a>00711 
-<a name="l00713"></a>00713 <span class="keyword">enum</span> RESAMPLING_METHOD { MULTINOMIAL = 0, STRATIFIED = 1, SYSTEMATIC = 3 };
-<a name="l00719"></a><a class="code" href="classbdm_1_1eEmp.html">00719</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="l00720"></a>00720 <span class="keyword">protected</span> :
-<a name="l00722"></a><a class="code" href="classbdm_1_1eEmp.html#9798006271ca77629855113f1283a031">00722</a>         <span class="keywordtype">int</span> <a class="code" href="classbdm_1_1eEmp.html#9798006271ca77629855113f1283a031" title="Number of particles.">n</a>;
-<a name="l00724"></a><a class="code" href="classbdm_1_1eEmp.html#9d39241aab7c4bbeb07c6d516421c67d">00724</a>         vec <a class="code" href="classbdm_1_1eEmp.html#9d39241aab7c4bbeb07c6d516421c67d" title="Sample weights .">w</a>;
-<a name="l00726"></a><a class="code" href="classbdm_1_1eEmp.html#73d819553a0f268b055a087d2d4486f3">00726</a>         Array&lt;vec&gt; <a class="code" href="classbdm_1_1eEmp.html#73d819553a0f268b055a087d2d4486f3" title="Samples .">samples</a>;
-<a name="l00727"></a>00727 <span class="keyword">public</span>:
-<a name="l00730"></a>00730         <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="l00731"></a>00731         <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="l00733"></a>00733 
-<a name="l00735"></a>00735         <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1eEmp.html#7cfd383180b486fe4526bdf0179350c0" title="Set samples and weights.">set_statistics</a> ( <span class="keyword">const</span> vec &amp;w0, <span class="keyword">const</span> epdf* pdf0 );
-<a name="l00737"></a><a class="code" href="classbdm_1_1eEmp.html#cef74aa5f87d10d440b9b1e8bc78c1e7">00737</a>         <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1eEmp.html#cef74aa5f87d10d440b9b1e8bc78c1e7" title="Set samples and weights.">set_statistics</a> ( <span class="keyword">const</span> <a class="code" href="classbdm_1_1epdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a>* pdf0 , <span class="keywordtype">int</span> <a class="code" href="classbdm_1_1eEmp.html#9798006271ca77629855113f1283a031" title="Number of particles.">n</a> ) {<a class="code" href="classbdm_1_1eEmp.html#cef74aa5f87d10d440b9b1e8bc78c1e7" title="Set samples and weights.">set_statistics</a> ( ones ( n ) /n,pdf0 );};
-<a name="l00739"></a>00739         <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="l00741"></a><a class="code" href="classbdm_1_1eEmp.html#c74c281d652356c19b6b079e42ca7ef1">00741</a>         <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1eEmp.html#c74c281d652356c19b6b079e42ca7ef1" title="Set sample.">set_parameters</a> ( <span class="keywordtype">int</span> n0, <span class="keywordtype">bool</span> copy=<span class="keyword">true</span> ) {<a class="code" href="classbdm_1_1eEmp.html#9798006271ca77629855113f1283a031" title="Number of particles.">n</a>=n0; <a class="code" href="classbdm_1_1eEmp.html#9d39241aab7c4bbeb07c6d516421c67d" title="Sample weights .">w</a>.set_size ( n0,copy );<a class="code" href="classbdm_1_1eEmp.html#73d819553a0f268b055a087d2d4486f3" title="Samples .">samples</a>.set_size ( n0,copy );};
-<a name="l00743"></a><a class="code" href="classbdm_1_1eEmp.html#d7f83cc0415cd44ae7cc8b4bdad93aef">00743</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="l00745"></a><a class="code" href="classbdm_1_1eEmp.html#b7d7106f486e3fad38590914a693d714">00745</a>         <span class="keyword">const</span> vec&amp; <a class="code" href="classbdm_1_1eEmp.html#b7d7106f486e3fad38590914a693d714" title="Potentially dangerous, use with care.">_w</a>()<span class="keyword"> const </span>{<span class="keywordflow">return</span> <a class="code" href="classbdm_1_1eEmp.html#9d39241aab7c4bbeb07c6d516421c67d" title="Sample weights .">w</a>;};
-<a name="l00747"></a><a class="code" href="classbdm_1_1eEmp.html#c24966b0aaeb767bc8a6b4fd60931be2">00747</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="l00749"></a><a class="code" href="classbdm_1_1eEmp.html#b59af0efdb009d98ea8ebfa965e74ae2">00749</a>         <span class="keyword">const</span> Array&lt;vec&gt;&amp; <a class="code" href="classbdm_1_1eEmp.html#b59af0efdb009d98ea8ebfa965e74ae2" title="access function">_samples</a>()<span class="keyword"> const </span>{<span class="keywordflow">return</span> <a class="code" href="classbdm_1_1eEmp.html#73d819553a0f268b055a087d2d4486f3" title="Samples .">samples</a>;};
-<a name="l00751"></a>00751         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="l00753"></a><a class="code" href="classbdm_1_1eEmp.html#97f1e07b5ae6eebc91c7365f0f88d270">00753</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="l00755"></a><a class="code" href="classbdm_1_1eEmp.html#01654c014d3aa068f8d4ecba4be86d09">00755</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="l00756"></a><a class="code" href="classbdm_1_1eEmp.html#bbfcb4f868c7381298c281a256d8c4b9">00756</a>         vec <a class="code" href="classbdm_1_1eEmp.html#bbfcb4f868c7381298c281a256d8c4b9" title="return expected value">mean</a>()<span class="keyword"> const </span>{
-<a name="l00757"></a>00757                 vec pom=zeros ( <a class="code" href="classbdm_1_1epdf.html#16adac20ec7fe07e1ea0b27d917788ce" title="dimension of the random variable">dim</a> );
-<a name="l00758"></a>00758                 <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="l00759"></a>00759                 <span class="keywordflow">return</span> pom;
-<a name="l00760"></a>00760         }
-<a name="l00761"></a><a class="code" href="classbdm_1_1eEmp.html#05e9ebf467ede737cb6a3621d7fd3c87">00761</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="l00762"></a>00762                 vec pom=zeros ( <a class="code" href="classbdm_1_1epdf.html#16adac20ec7fe07e1ea0b27d917788ce" title="dimension of the random variable">dim</a> );
-<a name="l00763"></a>00763                 <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="l00764"></a>00764                 <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="l00765"></a>00765         }
-<a name="l00767"></a><a class="code" href="classbdm_1_1eEmp.html#b1c9df656144edf79ba2d885613f661f">00767</a>         <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1eEmp.html#b1c9df656144edf79ba2d885613f661f" title="For this class, qbounds are minimum and maximum value of the population!">qbounds</a> ( vec &amp;lb, vec &amp;ub, <span class="keywordtype">double</span> perc=0.95 )<span class="keyword"> const </span>{
-<a name="l00768"></a>00768                 <span class="comment">// lb in inf so than it will be pushed below;</span>
-<a name="l00769"></a>00769                 lb.set_size(<a class="code" href="classbdm_1_1epdf.html#16adac20ec7fe07e1ea0b27d917788ce" title="dimension of the random variable">dim</a>);
-<a name="l00770"></a>00770                 ub.set_size(<a class="code" href="classbdm_1_1epdf.html#16adac20ec7fe07e1ea0b27d917788ce" title="dimension of the random variable">dim</a>);
-<a name="l00771"></a>00771                 lb = std::numeric_limits&lt;double&gt;::infinity();
-<a name="l00772"></a>00772                 ub = -std::numeric_limits&lt;double&gt;::infinity();
-<a name="l00773"></a>00773                 <span class="keywordtype">int</span> j;
-<a name="l00774"></a>00774                 <span class="keywordflow">for</span> ( <span class="keywordtype">int</span> i=0;i&lt;<a class="code" href="classbdm_1_1eEmp.html#9798006271ca77629855113f1283a031" title="Number of particles.">n</a>;i++ ) {
-<a name="l00775"></a>00775                         <span class="keywordflow">for</span> ( j=0;j&lt;<a class="code" href="classbdm_1_1epdf.html#16adac20ec7fe07e1ea0b27d917788ce" title="dimension of the random variable">dim</a>; j++ ) {
-<a name="l00776"></a>00776                                 <span class="keywordflow">if</span> ( <a class="code" href="classbdm_1_1eEmp.html#73d819553a0f268b055a087d2d4486f3" title="Samples .">samples</a> ( i ) ( j ) &lt;lb ( j ) ) {lb ( j ) =<a class="code" href="classbdm_1_1eEmp.html#73d819553a0f268b055a087d2d4486f3" title="Samples .">samples</a> ( i ) ( j );}
-<a name="l00777"></a>00777                                 <span class="keywordflow">if</span> ( <a class="code" href="classbdm_1_1eEmp.html#73d819553a0f268b055a087d2d4486f3" title="Samples .">samples</a> ( i ) ( j ) &gt;ub ( j ) ) {ub ( j ) =<a class="code" href="classbdm_1_1eEmp.html#73d819553a0f268b055a087d2d4486f3" title="Samples .">samples</a> ( i ) ( j );}
-<a name="l00778"></a>00778                         }
-<a name="l00779"></a>00779                 }
-<a name="l00780"></a>00780         }
-<a name="l00781"></a>00781 };
-<a name="l00782"></a>00782 
-<a name="l00783"></a>00783 
-<a name="l00785"></a>00785 
-<a name="l00786"></a>00786 <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt;
-<a name="l00787"></a>00787 <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="l00788"></a>00788 <span class="comment">//Fixme test dimensions of mu0 and R0;</span>
-<a name="l00789"></a>00789         mu = mu0;
-<a name="l00790"></a>00790         R = R0;
-<a name="l00791"></a>00791         <a class="code" href="classbdm_1_1epdf.html#16adac20ec7fe07e1ea0b27d917788ce" title="dimension of the random variable">dim</a> = mu0.length();
-<a name="l00792"></a>00792 };
-<a name="l00793"></a>00793 
-<a name="l00794"></a>00794 <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt;
-<a name="l00795"></a><a class="code" href="classbdm_1_1enorm.html#d2e0d3a1e30ab3ab04df2d0c43ae74a2">00795</a> <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1enorm.html#d2e0d3a1e30ab3ab04df2d0c43ae74a2" title="dupdate in exponential form (not really handy)">enorm&lt;sq_T&gt;::dupdate</a> ( mat &amp;v, <span class="keywordtype">double</span> nu ) {
-<a name="l00796"></a>00796         <span class="comment">//</span>
-<a name="l00797"></a>00797 };
-<a name="l00798"></a>00798 
-<a name="l00799"></a>00799 <span class="comment">// template&lt;class sq_T&gt;</span>
-<a name="l00800"></a>00800 <span class="comment">// void enorm&lt;sq_T&gt;::tupdate ( double phi, mat &amp;vbar, double nubar ) {</span>
-<a name="l00801"></a>00801 <span class="comment">//      //</span>
-<a name="l00802"></a>00802 <span class="comment">// };</span>
-<a name="l00803"></a>00803 
-<a name="l00804"></a>00804 <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt;
-<a name="l00805"></a><a class="code" href="classbdm_1_1enorm.html#e1a48f52351ec3a349bd443b713b1766">00805</a> vec <a class="code" href="classbdm_1_1enorm.html#e1a48f52351ec3a349bd443b713b1766" title="Returns a sample,  from density .">enorm&lt;sq_T&gt;::sample</a>()<span class="keyword"> const </span>{
-<a name="l00806"></a>00806         vec x ( <a class="code" href="classbdm_1_1epdf.html#16adac20ec7fe07e1ea0b27d917788ce" title="dimension of the random variable">dim</a> );
-<a name="l00807"></a>00807 <span class="preprocessor">#pragma omp critical</span>
-<a name="l00808"></a>00808 <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="l00809"></a>00809         vec smp = <a class="code" href="classbdm_1_1enorm.html#2d92dde696b2a7a5b10ddef5d22ba2c2" title="Covariance matrix in decomposed form.">R</a>.sqrt_mult ( x );
-<a name="l00810"></a>00810 
-<a name="l00811"></a>00811         smp += <a class="code" href="classbdm_1_1enorm.html#c702a194720853570d08b65482f842c7" title="mean value">mu</a>;
-<a name="l00812"></a>00812         <span class="keywordflow">return</span> smp;
-<a name="l00813"></a>00813 };
-<a name="l00814"></a>00814 
-<a name="l00815"></a>00815 <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt;
-<a name="l00816"></a>00816 mat <a class="code" href="classbdm_1_1enorm.html#e1a48f52351ec3a349bd443b713b1766" title="Returns a sample,  from density .">enorm&lt;sq_T&gt;::sample</a> ( <span class="keywordtype">int</span> N )<span class="keyword"> const </span>{
-<a name="l00817"></a>00817         mat X ( <a class="code" href="classbdm_1_1epdf.html#16adac20ec7fe07e1ea0b27d917788ce" title="dimension of the random variable">dim</a>,N );
-<a name="l00818"></a>00818         vec x ( <a class="code" href="classbdm_1_1epdf.html#16adac20ec7fe07e1ea0b27d917788ce" title="dimension of the random variable">dim</a> );
-<a name="l00819"></a>00819         vec pom;
-<a name="l00820"></a>00820         <span class="keywordtype">int</span> i;
-<a name="l00821"></a>00821 
-<a name="l00822"></a>00822         <span class="keywordflow">for</span> ( i=0;i&lt;N;i++ ) {
-<a name="l00823"></a>00823 <span class="preprocessor">#pragma omp critical</span>
-<a name="l00824"></a>00824 <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="l00825"></a>00825                 pom = <a class="code" href="classbdm_1_1enorm.html#2d92dde696b2a7a5b10ddef5d22ba2c2" title="Covariance matrix in decomposed form.">R</a>.sqrt_mult ( x );
-<a name="l00826"></a>00826                 pom +=<a class="code" href="classbdm_1_1enorm.html#c702a194720853570d08b65482f842c7" title="mean value">mu</a>;
-<a name="l00827"></a>00827                 X.set_col ( i, pom );
-<a name="l00828"></a>00828         }
-<a name="l00829"></a>00829 
-<a name="l00830"></a>00830         <span class="keywordflow">return</span> X;
-<a name="l00831"></a>00831 };
-<a name="l00832"></a>00832 
-<a name="l00833"></a>00833 <span class="comment">// template&lt;class sq_T&gt;</span>
-<a name="l00834"></a>00834 <span class="comment">// double enorm&lt;sq_T&gt;::eval ( const vec &amp;val ) const {</span>
-<a name="l00835"></a>00835 <span class="comment">//      double pdfl,e;</span>
-<a name="l00836"></a>00836 <span class="comment">//      pdfl = evallog ( val );</span>
-<a name="l00837"></a>00837 <span class="comment">//      e = exp ( pdfl );</span>
-<a name="l00838"></a>00838 <span class="comment">//      return e;</span>
-<a name="l00839"></a>00839 <span class="comment">// };</span>
-<a name="l00840"></a>00840 
-<a name="l00841"></a>00841 <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt;
-<a name="l00842"></a><a class="code" href="classbdm_1_1enorm.html#e13aeed5b543b2179bacdc4fa2ae47a3">00842</a> <span class="keywordtype">double</span> <a class="code" href="classbdm_1_1enorm.html#e13aeed5b543b2179bacdc4fa2ae47a3" title="Evaluate normalized log-probability.">enorm&lt;sq_T&gt;::evallog_nn</a> ( <span class="keyword">const</span> vec &amp;val )<span class="keyword"> const </span>{
-<a name="l00843"></a>00843         <span class="comment">// 1.83787706640935 = log(2pi)</span>
-<a name="l00844"></a>00844         <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="l00845"></a>00845         <span class="keywordflow">return</span>  tmp;
-<a name="l00846"></a>00846 };
+<a name="l00708"></a><a class="code" href="classbdm_1_1elognorm.html">00708</a>         <span class="keyword">class </span><a class="code" href="classbdm_1_1elognorm.html">elognorm</a>: <span class="keyword">public</span> <a class="code" href="classbdm_1_1enorm.html" title="Gaussian density with positive definite (decomposed) covariance matrix.">enorm</a>&lt;ldmat&gt;
+<a name="l00709"></a>00709         {
+<a name="l00710"></a>00710                 <span class="keyword">public</span>:
+<a name="l00711"></a><a class="code" href="classbdm_1_1elognorm.html#8b948e2bce1253765a2542199913aaba">00711</a>                         vec <a class="code" href="classbdm_1_1elognorm.html#8b948e2bce1253765a2542199913aaba" title="Returns a sample,  from density .">sample</a>()<span class="keyword"> const </span>{<span class="keywordflow">return</span> exp ( <a class="code" href="classbdm_1_1enorm.html" title="Gaussian density with positive definite (decomposed) covariance matrix.">enorm&lt;ldmat&gt;::sample</a>() );};
+<a name="l00712"></a><a class="code" href="classbdm_1_1elognorm.html#adb41e4f4d6600dec6f8c1dbc5ed9eea">00712</a>                         vec <a class="code" href="classbdm_1_1elognorm.html#adb41e4f4d6600dec6f8c1dbc5ed9eea" title="return expected value">mean</a>()<span class="keyword"> const </span>{vec var=<a class="code" href="classbdm_1_1enorm.html#729c75ef0fa8abae03d58ad1f81e6773" title="return expected variance (not covariance!)">enorm&lt;ldmat&gt;::variance</a>();<span class="keywordflow">return</span> exp ( <a class="code" href="classbdm_1_1enorm.html#c702a194720853570d08b65482f842c7" title="mean value">mu</a> - 0.5*var );};
+<a name="l00713"></a>00713 
+<a name="l00714"></a>00714         };
+<a name="l00715"></a>00715 
+<a name="l00727"></a><a class="code" href="classbdm_1_1mlognorm.html">00727</a>         <span class="keyword">class </span><a class="code" href="classbdm_1_1mlognorm.html" title="Log-Normal random walk.">mlognorm</a> : <span class="keyword">public</span> <a class="code" href="classbdm_1_1mpdf.html" title="Conditional probability density, e.g. modeling some dependencies.">mpdf</a>
+<a name="l00728"></a>00728         {
+<a name="l00729"></a>00729                 <span class="keyword">protected</span>:
+<a name="l00730"></a>00730                         <a class="code" href="classbdm_1_1elognorm.html">elognorm</a> eno;
+<a name="l00732"></a><a class="code" href="classbdm_1_1mlognorm.html#a51128a2e503b8b2ce698244b9e0db1a">00732</a>                         <span class="keywordtype">double</span> <a class="code" href="classbdm_1_1mlognorm.html#a51128a2e503b8b2ce698244b9e0db1a" title="parameter 1/2*sigma^2">sig2</a>;
+<a name="l00734"></a><a class="code" href="classbdm_1_1mlognorm.html#7d0063f77d899ef22e8c5edd642176d2">00734</a>                         vec &amp;<a class="code" href="classbdm_1_1mlognorm.html#7d0063f77d899ef22e8c5edd642176d2" title="access">mu</a>;
+<a name="l00735"></a>00735                 <span class="keyword">public</span>:
+<a name="l00737"></a><a class="code" href="classbdm_1_1mlognorm.html#a5d6eb2688d02e0348b96c4fbd7bde41">00737</a>                         <a class="code" href="classbdm_1_1mlognorm.html#a5d6eb2688d02e0348b96c4fbd7bde41" title="Constructor.">mlognorm</a> ( ) : eno (), <a class="code" href="classbdm_1_1mlognorm.html#7d0063f77d899ef22e8c5edd642176d2" title="access">mu</a> ( eno._mu() ) {<a class="code" href="classbdm_1_1mpdf.html#5eea43c56d38e4441bfb30270db949c0" title="pointer to internal epdf">ep</a>=&amp;eno;};
+<a name="l00739"></a><a class="code" href="classbdm_1_1mlognorm.html#604cab0e8a76f9041dc3c606043bb39f">00739</a>                         <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1mlognorm.html#604cab0e8a76f9041dc3c606043bb39f" title="Set value of k.">set_parameters</a> ( <span class="keywordtype">int</span> size, <span class="keywordtype">double</span> k )
+<a name="l00740"></a>00740                         {
+<a name="l00741"></a>00741                                 <a class="code" href="classbdm_1_1mlognorm.html#a51128a2e503b8b2ce698244b9e0db1a" title="parameter 1/2*sigma^2">sig2</a> = 0.5*log ( k*k+1 );
+<a name="l00742"></a>00742                                 eno.<a class="code" href="classbdm_1_1enorm.html#b8322f2c11560871dd922c660f4771bb">set_parameters</a> ( zeros ( size ),2*<a class="code" href="classbdm_1_1mlognorm.html#a51128a2e503b8b2ce698244b9e0db1a" title="parameter 1/2*sigma^2">sig2</a>*eye ( size ) );
+<a name="l00743"></a>00743 
+<a name="l00744"></a>00744                                 <a class="code" href="classbdm_1_1mpdf.html#7c1900976ff13dbc09c9729b3bbff9e6" title="dimension of the condition">dimc</a> = size;
+<a name="l00745"></a>00745                         };
+<a name="l00746"></a>00746 
+<a name="l00747"></a><a class="code" href="classbdm_1_1mlognorm.html#9106d8fd8bdf2b6be675ffd8f3ca584e">00747</a>                         <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1mlognorm.html#9106d8fd8bdf2b6be675ffd8f3ca584e" title="Update ep so that it represents this mpdf conditioned on rvc = cond.">condition</a> ( <span class="keyword">const</span> vec &amp;val )
+<a name="l00748"></a>00748                         {
+<a name="l00749"></a>00749                                 <a class="code" href="classbdm_1_1mlognorm.html#7d0063f77d899ef22e8c5edd642176d2" title="access">mu</a>=log ( val )-<a class="code" href="classbdm_1_1mlognorm.html#a51128a2e503b8b2ce698244b9e0db1a" title="parameter 1/2*sigma^2">sig2</a>;<span class="comment">//elem_mult ( refl,pow ( val,l ) );</span>
+<a name="l00750"></a>00750                         };
+<a name="l00751"></a>00751         };
+<a name="l00752"></a>00752 
+<a name="l00756"></a><a class="code" href="classbdm_1_1eWishartCh.html">00756</a>         <span class="keyword">class </span><a class="code" href="classbdm_1_1eWishartCh.html">eWishartCh</a> : <span class="keyword">public</span> <a class="code" href="classbdm_1_1epdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a>
+<a name="l00757"></a>00757         {
+<a name="l00758"></a>00758                 <span class="keyword">protected</span>:
+<a name="l00760"></a><a class="code" href="classbdm_1_1eWishartCh.html#1b42f9284a32f23b0b253a628cda7490">00760</a>                         <a class="code" href="classchmat.html" title="Symmetric matrix stored in square root decomposition using upper cholesky.">chmat</a> <a class="code" href="classbdm_1_1eWishartCh.html#1b42f9284a32f23b0b253a628cda7490" title="Upper-Triagle of Choleski decomposition of .">Y</a>;
+<a name="l00762"></a><a class="code" href="classbdm_1_1eWishartCh.html#b745c73faef785009484180582050a1f">00762</a>                         <span class="keywordtype">int</span> <a class="code" href="classbdm_1_1eWishartCh.html#b745c73faef785009484180582050a1f" title="dimension of matrix ">p</a>;
+<a name="l00764"></a><a class="code" href="classbdm_1_1eWishartCh.html#1879a14d7d2bb05062523b189baa11c3">00764</a>                         <span class="keywordtype">double</span> <a class="code" href="classbdm_1_1eWishartCh.html#1879a14d7d2bb05062523b189baa11c3" title="degrees of freedom ">delta</a>;
+<a name="l00765"></a>00765                 <span class="keyword">public</span>:
+<a name="l00766"></a>00766                         <span class="keywordtype">void</span> set_parameters ( <span class="keyword">const</span> mat &amp;Y0, <span class="keyword">const</span> <span class="keywordtype">double</span> delta0 ) {<a class="code" href="classbdm_1_1eWishartCh.html#1b42f9284a32f23b0b253a628cda7490" title="Upper-Triagle of Choleski decomposition of .">Y</a>=<a class="code" href="classchmat.html" title="Symmetric matrix stored in square root decomposition using upper cholesky.">chmat</a> ( Y0 );<a class="code" href="classbdm_1_1eWishartCh.html#1879a14d7d2bb05062523b189baa11c3" title="degrees of freedom ">delta</a>=delta0; <a class="code" href="classbdm_1_1eWishartCh.html#b745c73faef785009484180582050a1f" title="dimension of matrix ">p</a>=<a class="code" href="classbdm_1_1eWishartCh.html#1b42f9284a32f23b0b253a628cda7490" title="Upper-Triagle of Choleski decomposition of .">Y</a>.<a class="code" href="classsqmat.html#071e80ced9cc3b8cbb360fa7462eb646" title="Reimplementing common functions of mat: cols().">rows</a>(); <a class="code" href="classbdm_1_1epdf.html#16adac20ec7fe07e1ea0b27d917788ce" title="dimension of the random variable">dim</a> = <a class="code" href="classbdm_1_1eWishartCh.html#b745c73faef785009484180582050a1f" title="dimension of matrix ">p</a>*<a class="code" href="classbdm_1_1eWishartCh.html#b745c73faef785009484180582050a1f" title="dimension of matrix ">p</a>; }
+<a name="l00767"></a>00767                         mat sample_mat()<span class="keyword"> const</span>
+<a name="l00768"></a>00768 <span class="keyword">                        </span>{
+<a name="l00769"></a>00769                                 mat X=zeros ( <a class="code" href="classbdm_1_1eWishartCh.html#b745c73faef785009484180582050a1f" title="dimension of matrix ">p</a>,<a class="code" href="classbdm_1_1eWishartCh.html#b745c73faef785009484180582050a1f" title="dimension of matrix ">p</a> );
+<a name="l00770"></a>00770 
+<a name="l00771"></a>00771                                 <span class="comment">//sample diagonal</span>
+<a name="l00772"></a>00772                                 <span class="keywordflow">for</span> ( <span class="keywordtype">int</span> i=0;i&lt;<a class="code" href="classbdm_1_1eWishartCh.html#b745c73faef785009484180582050a1f" title="dimension of matrix ">p</a>;i++ )
+<a name="l00773"></a>00773                                 {
+<a name="l00774"></a>00774                                         GamRNG.<a class="code" href="classitpp_1_1Gamma__RNG.html#dfaae19411e39aa87e1f72e409b6babe" title="Set lambda.">setup</a> ( 0.5* ( <a class="code" href="classbdm_1_1eWishartCh.html#1879a14d7d2bb05062523b189baa11c3" title="degrees of freedom ">delta</a>-i ) , 0.5 ); <span class="comment">// no +1 !! index if from 0</span>
+<a name="l00775"></a>00775 <span class="preprocessor">#pragma omp critical</span>
+<a name="l00776"></a>00776 <span class="preprocessor"></span>                                        X ( i,i ) =sqrt ( GamRNG() );
+<a name="l00777"></a>00777                                 }
+<a name="l00778"></a>00778                                 <span class="comment">//do the rest</span>
+<a name="l00779"></a>00779                                 <span class="keywordflow">for</span> ( <span class="keywordtype">int</span> i=0;i&lt;p;i++ )
+<a name="l00780"></a>00780                                 {
+<a name="l00781"></a>00781                                         <span class="keywordflow">for</span> ( <span class="keywordtype">int</span> j=i+1;j&lt;p;j++ )
+<a name="l00782"></a>00782                                         {
+<a name="l00783"></a>00783 <span class="preprocessor">#pragma omp critical</span>
+<a name="l00784"></a>00784 <span class="preprocessor"></span>                                                X ( i,j ) =NorRNG.sample();
+<a name="l00785"></a>00785                                         }
+<a name="l00786"></a>00786                                 }
+<a name="l00787"></a>00787                                 <span class="keywordflow">return</span> X*<a class="code" href="classbdm_1_1eWishartCh.html#1b42f9284a32f23b0b253a628cda7490" title="Upper-Triagle of Choleski decomposition of .">Y</a>.<a class="code" href="classchmat.html#9c50d31c999d85d8e9d8cf2b69b6ac8c" title="Access function.">_Ch</a>();<span class="comment">// return upper triangular part of the decomposition</span>
+<a name="l00788"></a>00788                         }
+<a name="l00789"></a><a class="code" href="classbdm_1_1eWishartCh.html#8f2154b8b5be8f4c5788f261b6d57b9a">00789</a>                         vec <a class="code" href="classbdm_1_1eWishartCh.html#8f2154b8b5be8f4c5788f261b6d57b9a" title="Returns a sample,  from density .">sample</a> ()<span class="keyword"> const</span>
+<a name="l00790"></a>00790 <span class="keyword">                        </span>{
+<a name="l00791"></a>00791                                 <span class="keywordflow">return</span> vec ( sample_mat()._data(),p*p );
+<a name="l00792"></a>00792                         }
+<a name="l00794"></a><a class="code" href="classbdm_1_1eWishartCh.html#4eee757c0535c2a88bb20f0767c64981">00794</a>                         <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1eWishartCh.html#4eee757c0535c2a88bb20f0767c64981" title="fast access function y0 will be copied into Y.Ch.">setY</a> ( <span class="keyword">const</span> mat &amp;Ch0 ) {copy_vector ( <a class="code" href="classbdm_1_1epdf.html#16adac20ec7fe07e1ea0b27d917788ce" title="dimension of the random variable">dim</a>,Ch0._data(), <a class="code" href="classbdm_1_1eWishartCh.html#1b42f9284a32f23b0b253a628cda7490" title="Upper-Triagle of Choleski decomposition of .">Y</a>.<a class="code" href="classchmat.html#9c50d31c999d85d8e9d8cf2b69b6ac8c" title="Access function.">_Ch</a>()._data() );}
+<a name="l00796"></a><a class="code" href="classbdm_1_1eWishartCh.html#7eac414ec10b85aa5536b0092c57bc4a">00796</a>                         <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1eWishartCh.html#7eac414ec10b85aa5536b0092c57bc4a" title="fast access function y0 will be copied into Y.Ch.">_setY</a> ( <span class="keyword">const</span> vec &amp;ch0 ) {copy_vector ( <a class="code" href="classbdm_1_1epdf.html#16adac20ec7fe07e1ea0b27d917788ce" title="dimension of the random variable">dim</a>, ch0._data(), <a class="code" href="classbdm_1_1eWishartCh.html#1b42f9284a32f23b0b253a628cda7490" title="Upper-Triagle of Choleski decomposition of .">Y</a>.<a class="code" href="classchmat.html#9c50d31c999d85d8e9d8cf2b69b6ac8c" title="Access function.">_Ch</a>()._data() ); }
+<a name="l00798"></a><a class="code" href="classbdm_1_1eWishartCh.html#1708cacb5d8cb1b96395d35f5327cb7e">00798</a>                         <span class="keyword">const</span> <a class="code" href="classchmat.html" title="Symmetric matrix stored in square root decomposition using upper cholesky.">chmat</a>&amp; <a class="code" href="classbdm_1_1eWishartCh.html#1708cacb5d8cb1b96395d35f5327cb7e" title="access function">getY</a>()<span class="keyword">const </span>{<span class="keywordflow">return</span> <a class="code" href="classbdm_1_1eWishartCh.html#1b42f9284a32f23b0b253a628cda7490" title="Upper-Triagle of Choleski decomposition of .">Y</a>;}
+<a name="l00799"></a>00799         };
+<a name="l00800"></a>00800 
+<a name="l00801"></a>00801         <span class="keyword">class </span>eiWishartCh: <span class="keyword">public</span> epdf
+<a name="l00802"></a>00802         {
+<a name="l00803"></a>00803                 <span class="keyword">protected</span>:
+<a name="l00804"></a>00804                         eWishartCh W;
+<a name="l00805"></a>00805                         <span class="keywordtype">int</span> p;
+<a name="l00806"></a>00806                         <span class="keywordtype">double</span> delta;
+<a name="l00807"></a>00807                 <span class="keyword">public</span>:
+<a name="l00808"></a>00808                         <span class="keywordtype">void</span> set_parameters ( <span class="keyword">const</span> mat &amp;Y0, <span class="keyword">const</span> <span class="keywordtype">double</span> delta0) {
+<a name="l00809"></a>00809                                 delta = delta0;
+<a name="l00810"></a>00810                                 W.set_parameters ( inv ( Y0 ),delta0 ); 
+<a name="l00811"></a>00811                                 dim = W.dimension(); p=Y0.rows();
+<a name="l00812"></a>00812                         }
+<a name="l00813"></a>00813                         vec sample()<span class="keyword"> const </span>{mat iCh; iCh=inv ( W.sample_mat() ); <span class="keywordflow">return</span> vec ( iCh._data(),<a class="code" href="classbdm_1_1epdf.html#16adac20ec7fe07e1ea0b27d917788ce" title="dimension of the random variable">dim</a> );}
+<a name="l00814"></a>00814                         <span class="keywordtype">void</span> _setY ( <span class="keyword">const</span> vec &amp;y0 )
+<a name="l00815"></a>00815                         {
+<a name="l00816"></a>00816                                 mat Ch ( p,p );
+<a name="l00817"></a>00817                                 mat iCh ( p,p );
+<a name="l00818"></a>00818                                 copy_vector ( dim, y0._data(), Ch._data() );
+<a name="l00819"></a>00819                                 
+<a name="l00820"></a>00820                                 iCh=inv ( Ch );
+<a name="l00821"></a>00821                                 W.setY ( iCh );
+<a name="l00822"></a>00822                         }
+<a name="l00823"></a>00823                         <span class="keyword">virtual</span> <span class="keywordtype">double</span> evallog ( <span class="keyword">const</span> vec &amp;val )<span class="keyword"> const </span>{
+<a name="l00824"></a>00824                                 <a class="code" href="classchmat.html" title="Symmetric matrix stored in square root decomposition using upper cholesky.">chmat</a> X(p);
+<a name="l00825"></a>00825                                 <span class="keyword">const</span> <a class="code" href="classchmat.html" title="Symmetric matrix stored in square root decomposition using upper cholesky.">chmat</a>&amp; Y=W.getY();
+<a name="l00826"></a>00826                                  
+<a name="l00827"></a>00827                                 copy_vector(p*p,val._data(),X._Ch()._data());
+<a name="l00828"></a>00828                                 <a class="code" href="classchmat.html" title="Symmetric matrix stored in square root decomposition using upper cholesky.">chmat</a> iX(p);X.inv(iX);
+<a name="l00829"></a>00829                                 <span class="comment">// compute  </span>
+<a name="l00830"></a>00830 <span class="comment">//                              \frac{ |\Psi|^{m/2}|X|^{-(m+p+1)/2}e^{-tr(\Psi X^{-1})/2} }{ 2^{mp/2}\Gamma_p(m/2)},</span>
+<a name="l00831"></a>00831                                 mat M=Y.<a class="code" href="classchmat.html#045addd685f8d978efda232d7dcb070e" title="Conversion to full matrix.">to_mat</a>()*iX.to_mat();
+<a name="l00832"></a>00832                                 
+<a name="l00833"></a>00833                                 <span class="keywordtype">double</span> log1 = 0.5*p*(2*Y.<a class="code" href="classchmat.html#b504ca818203b13e667cb3c503980382" title="Logarithm of a determinant.">logdet</a>())-0.5*(delta+p+1)*(2*X.logdet())-0.5*trace(M); 
+<a name="l00834"></a>00834                                 <span class="comment">//Fixme! Multivariate gamma omitted!! it is ok for sampling, but not otherwise!!</span>
+<a name="l00835"></a>00835                                 
+<a name="l00836"></a>00836 <span class="comment">/*                              if (0) {</span>
+<a name="l00837"></a>00837 <span class="comment">                                        mat XX=X.to_mat();</span>
+<a name="l00838"></a>00838 <span class="comment">                                        mat YY=Y.to_mat();</span>
+<a name="l00839"></a>00839 <span class="comment">                                        </span>
+<a name="l00840"></a>00840 <span class="comment">                                        double log2 = 0.5*p*log(det(YY))-0.5*(delta+p+1)*log(det(XX))-0.5*trace(YY*inv(XX)); </span>
+<a name="l00841"></a>00841 <span class="comment">                                        cout &lt;&lt; log1 &lt;&lt; "," &lt;&lt; log2 &lt;&lt; endl;</span>
+<a name="l00842"></a>00842 <span class="comment">                                }*/</span>
+<a name="l00843"></a>00843                                 <span class="keywordflow">return</span> log1;                            
+<a name="l00844"></a>00844                         };
+<a name="l00845"></a>00845                         
+<a name="l00846"></a>00846         };
 <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><a class="code" href="classbdm_1_1enorm.html#25785343aff102cc5df1cab08ba16d32">00849</a> <span class="keyword">inline</span> <span class="keywordtype">double</span> <a class="code" href="classbdm_1_1enorm.html#25785343aff102cc5df1cab08ba16d32" title="logarithm of the normalizing constant, ">enorm&lt;sq_T&gt;::lognc</a> ()<span class="keyword"> const </span>{
-<a name="l00850"></a>00850         <span class="comment">// 1.83787706640935 = log(2pi)</span>
-<a name="l00851"></a>00851         <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="l00852"></a>00852         <span class="keywordflow">return</span> tmp;
-<a name="l00853"></a>00853 };
-<a name="l00854"></a>00854 
-<a name="l00855"></a>00855 <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt;
-<a name="l00856"></a><a class="code" href="classbdm_1_1mlnorm.html#5d18dec3167584338a4775c1d165d96f">00856</a> <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1mlnorm.html#5d18dec3167584338a4775c1d165d96f" title="Set A and R.">mlnorm&lt;sq_T&gt;::set_parameters</a> ( <span class="keyword">const</span> mat &amp;A0, <span class="keyword">const</span> vec &amp;mu0, <span class="keyword">const</span> sq_T &amp;R0 ) {
-<a name="l00857"></a>00857         it_assert_debug ( A0.rows() ==mu0.length(),<span class="stringliteral">""</span> );
-<a name="l00858"></a>00858         it_assert_debug ( A0.rows() ==R0.rows(),<span class="stringliteral">""</span> );
-<a name="l00859"></a>00859 
-<a name="l00860"></a>00860         <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="l00861"></a>00861         A = A0;
-<a name="l00862"></a>00862         mu_const = mu0;
-<a name="l00863"></a>00863         <a class="code" href="classbdm_1_1mpdf.html#7c1900976ff13dbc09c9729b3bbff9e6" title="dimension of the condition">dimc</a>=A0.cols();
-<a name="l00864"></a>00864 }
-<a name="l00865"></a>00865 
-<a name="l00866"></a>00866 <span class="comment">// template&lt;class sq_T&gt;</span>
-<a name="l00867"></a>00867 <span class="comment">// vec mlnorm&lt;sq_T&gt;::samplecond (const  vec &amp;cond, double &amp;lik ) {</span>
-<a name="l00868"></a>00868 <span class="comment">//      this-&gt;condition ( cond );</span>
-<a name="l00869"></a>00869 <span class="comment">//      vec smp = epdf.sample();</span>
-<a name="l00870"></a>00870 <span class="comment">//      lik = epdf.eval ( smp );</span>
-<a name="l00871"></a>00871 <span class="comment">//      return smp;</span>
-<a name="l00872"></a>00872 <span class="comment">// }</span>
-<a name="l00873"></a>00873 
-<a name="l00874"></a>00874 <span class="comment">// template&lt;class sq_T&gt;</span>
-<a name="l00875"></a>00875 <span class="comment">// mat mlnorm&lt;sq_T&gt;::samplecond (const vec &amp;cond, vec &amp;lik, int n ) {</span>
-<a name="l00876"></a>00876 <span class="comment">//      int i;</span>
-<a name="l00877"></a>00877 <span class="comment">//      int dim = rv.count();</span>
-<a name="l00878"></a>00878 <span class="comment">//      mat Smp ( dim,n );</span>
-<a name="l00879"></a>00879 <span class="comment">//      vec smp ( dim );</span>
-<a name="l00880"></a>00880 <span class="comment">//      this-&gt;condition ( cond );</span>
-<a name="l00881"></a>00881 <span class="comment">//</span>
-<a name="l00882"></a>00882 <span class="comment">//      for ( i=0; i&lt;n; i++ ) {</span>
-<a name="l00883"></a>00883 <span class="comment">//              smp = epdf.sample();</span>
-<a name="l00884"></a>00884 <span class="comment">//              lik ( i ) = epdf.eval ( smp );</span>
-<a name="l00885"></a>00885 <span class="comment">//              Smp.set_col ( i ,smp );</span>
-<a name="l00886"></a>00886 <span class="comment">//      }</span>
-<a name="l00887"></a>00887 <span class="comment">//</span>
-<a name="l00888"></a>00888 <span class="comment">//      return Smp;</span>
-<a name="l00889"></a>00889 <span class="comment">// }</span>
-<a name="l00890"></a>00890 
-<a name="l00891"></a>00891 <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt;
-<a name="l00892"></a><a class="code" href="classbdm_1_1mlnorm.html#0dafc0196e7e07fd06dc6716e0e18bc2">00892</a> <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1mlnorm.html#0dafc0196e7e07fd06dc6716e0e18bc2">mlnorm&lt;sq_T&gt;::condition</a> ( <span class="keyword">const</span> vec &amp;cond ) {
-<a name="l00893"></a>00893         _mu = A*cond + mu_const;
-<a name="l00894"></a>00894 <span class="comment">//R is already assigned;</span>
-<a name="l00895"></a>00895 }
-<a name="l00896"></a>00896 
-<a name="l00897"></a>00897 <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt;
-<a name="l00898"></a><a class="code" href="classbdm_1_1enorm.html#cd02d76e9d4f96bdd3fa6b604e273039">00898</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#cd02d76e9d4f96bdd3fa6b604e273039" title="Return marginal density on the given RV, the remainig rvs are intergrated out.">enorm&lt;sq_T&gt;::marginal</a> ( <span class="keyword">const</span> <a class="code" href="classbdm_1_1RV.html" title="Class representing variables, most often random variables.">RV</a> &amp;rvn )<span class="keyword"> const </span>{
-<a name="l00899"></a>00899         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="l00900"></a>00900         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="l00901"></a>00901 
-<a name="l00902"></a>00902         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="l00903"></a>00903 
-<a name="l00904"></a>00904         <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="l00905"></a>00905         tmp-&gt;<a class="code" href="classbdm_1_1epdf.html#f423e28448dbb69ef4905295ec8de8ff" title="Name its rv.">set_rv</a> ( rvn );
-<a name="l00906"></a>00906         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="l00907"></a>00907         <span class="keywordflow">return</span> tmp;
-<a name="l00908"></a>00908 }
-<a name="l00909"></a>00909 
-<a name="l00910"></a>00910 <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt;
-<a name="l00911"></a><a class="code" href="classbdm_1_1enorm.html#baea4d49c657342b58297d68cda16d26">00911</a> <a class="code" href="classbdm_1_1mpdf.html" title="Conditional probability density, e.g. modeling some dependencies.">mpdf</a>* <a class="code" href="classbdm_1_1enorm.html#baea4d49c657342b58297d68cda16d26" title="Return conditional density on the given RV, the remaining rvs will be in conditioning...">enorm&lt;sq_T&gt;::condition</a> ( <span class="keyword">const</span> <a class="code" href="classbdm_1_1RV.html" title="Class representing variables, most often random variables.">RV</a> &amp;rvn )<span class="keyword"> const </span>{
-<a name="l00912"></a>00912 
-<a name="l00913"></a>00913         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="l00914"></a>00914 
-<a name="l00915"></a>00915         <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="l00916"></a>00916         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="l00917"></a>00917         <span class="comment">//Permutation vector of the new R</span>
-<a name="l00918"></a>00918         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="l00919"></a>00919         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="l00920"></a>00920         ivec perm=concat ( irvn , irvc );
-<a name="l00921"></a>00921         sq_T Rn ( <a class="code" href="classbdm_1_1enorm.html#2d92dde696b2a7a5b10ddef5d22ba2c2" title="Covariance matrix in decomposed form.">R</a>,perm );
-<a name="l00922"></a>00922 
-<a name="l00923"></a>00923         <span class="comment">//fixme - could this be done in general for all sq_T?</span>
-<a name="l00924"></a>00924         mat S=Rn.to_mat();
-<a name="l00925"></a>00925         <span class="comment">//fixme</span>
-<a name="l00926"></a>00926         <span class="keywordtype">int</span> n=rvn.<a class="code" href="classbdm_1_1RV.html#de30156104f61d86c94f758861418089">_dsize</a>()-1;
-<a name="l00927"></a>00927         <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="l00928"></a>00928         mat S11 = S.get ( 0,n, 0, n );
-<a name="l00929"></a>00929         mat S12 = S.get ( 0, n , rvn.<a class="code" href="classbdm_1_1RV.html#de30156104f61d86c94f758861418089">_dsize</a>(), end );
-<a name="l00930"></a>00930         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="l00931"></a>00931 
-<a name="l00932"></a>00932         vec mu1 = <a class="code" href="classbdm_1_1enorm.html#c702a194720853570d08b65482f842c7" title="mean value">mu</a> ( irvn );
-<a name="l00933"></a>00933         vec mu2 = <a class="code" href="classbdm_1_1enorm.html#c702a194720853570d08b65482f842c7" title="mean value">mu</a> ( irvc );
-<a name="l00934"></a>00934         mat A=S12*inv ( S22 );
-<a name="l00935"></a>00935         sq_T R_n ( S11 - A *S12.T() );
-<a name="l00936"></a>00936 
-<a name="l00937"></a>00937         <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="l00938"></a>00938         tmp-&gt;set_rv ( rvn ); tmp-&gt;set_rvc ( rvc );
-<a name="l00939"></a>00939         tmp-&gt;set_parameters ( A,mu1-A*mu2,R_n );
-<a name="l00940"></a>00940         <span class="keywordflow">return</span> tmp;
-<a name="l00941"></a>00941 }
-<a name="l00942"></a>00942 
-<a name="l00944"></a>00944 
-<a name="l00945"></a>00945 <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt;
-<a name="l00946"></a>00946 std::ostream &amp;operator&lt;&lt; ( std::ostream &amp;os,  mlnorm&lt;sq_T&gt; &amp;ml ) {
-<a name="l00947"></a>00947         os &lt;&lt; <span class="stringliteral">"A:"</span>&lt;&lt; ml.A&lt;&lt;endl;
-<a name="l00948"></a>00948         os &lt;&lt; <span class="stringliteral">"mu:"</span>&lt;&lt; ml.mu_const&lt;&lt;endl;
-<a name="l00949"></a>00949         os &lt;&lt; <span class="stringliteral">"R:"</span> &lt;&lt; ml.epdf._R().to_mat() &lt;&lt;endl;
-<a name="l00950"></a>00950         <span class="keywordflow">return</span> os;
-<a name="l00951"></a>00951 };
-<a name="l00952"></a>00952 
-<a name="l00953"></a>00953 }
-<a name="l00954"></a>00954 <span class="preprocessor">#endif //EF_H</span>
+<a name="l00848"></a>00848         <span class="keyword">class </span>rwiWishartCh : <span class="keyword">public</span> mpdf
+<a name="l00849"></a>00849         {
+<a name="l00850"></a>00850                 <span class="keyword">protected</span>:
+<a name="l00851"></a>00851                         eiWishartCh eiW;
+<a name="l00853"></a>00853                         <span class="keywordtype">double</span> sqd;
+<a name="l00854"></a>00854                         <span class="comment">//reference point for diagonal</span>
+<a name="l00855"></a>00855                         vec refl;
+<a name="l00856"></a>00856                         <span class="keywordtype">double</span> l;
+<a name="l00857"></a>00857                         <span class="keywordtype">int</span> p;
+<a name="l00858"></a>00858                 <span class="keyword">public</span>:
+<a name="l00859"></a>00859                         <span class="keywordtype">void</span> set_parameters ( <span class="keywordtype">int</span> p0, <span class="keywordtype">double</span> k, vec ref0, <span class="keywordtype">double</span> l0  )
+<a name="l00860"></a>00860                         {
+<a name="l00861"></a>00861                                 p=p0;
+<a name="l00862"></a>00862                                 <span class="keywordtype">double</span> delta = 2/(k*k)+p+3;
+<a name="l00863"></a>00863                                 sqd=sqrt ( delta-p-1 );
+<a name="l00864"></a>00864                                 l=l0;
+<a name="l00865"></a>00865                                 refl=pow(ref0,1-l);
+<a name="l00866"></a>00866                                 
+<a name="l00867"></a>00867                                 eiW.set_parameters ( eye ( p ),delta );
+<a name="l00868"></a>00868                                 <a class="code" href="classbdm_1_1mpdf.html#5eea43c56d38e4441bfb30270db949c0" title="pointer to internal epdf">ep</a>=&amp;eiW;
+<a name="l00869"></a>00869                                 <a class="code" href="classbdm_1_1mpdf.html#7c1900976ff13dbc09c9729b3bbff9e6" title="dimension of the condition">dimc</a>=eiW.dimension();
+<a name="l00870"></a>00870                         }
+<a name="l00871"></a>00871                         <span class="keywordtype">void</span> condition ( <span class="keyword">const</span> vec &amp;c ) {
+<a name="l00872"></a>00872                                 vec z=c;
+<a name="l00873"></a>00873                                 <span class="keywordtype">int</span> ri=0;
+<a name="l00874"></a>00874                                 <span class="keywordflow">for</span>(<span class="keywordtype">int</span> i=0;i&lt;p*p;i+=(p+1)){<span class="comment">//trace diagonal element</span>
+<a name="l00875"></a>00875                                         z(i) = pow(z(i),l)*refl(ri);
+<a name="l00876"></a>00876                                         ri++;
+<a name="l00877"></a>00877                                 }
+<a name="l00878"></a>00878 
+<a name="l00879"></a>00879                                 eiW._setY ( sqd*z );
+<a name="l00880"></a>00880                         }
+<a name="l00881"></a>00881         };
+<a name="l00882"></a>00882 
+<a name="l00884"></a>00884         <span class="keyword">enum</span> RESAMPLING_METHOD { MULTINOMIAL = 0, STRATIFIED = 1, SYSTEMATIC = 3 };
+<a name="l00890"></a><a class="code" href="classbdm_1_1eEmp.html">00890</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="l00891"></a>00891         {
+<a name="l00892"></a>00892                 <span class="keyword">protected</span> :
+<a name="l00894"></a><a class="code" href="classbdm_1_1eEmp.html#9798006271ca77629855113f1283a031">00894</a>                         <span class="keywordtype">int</span> <a class="code" href="classbdm_1_1eEmp.html#9798006271ca77629855113f1283a031" title="Number of particles.">n</a>;
+<a name="l00896"></a><a class="code" href="classbdm_1_1eEmp.html#9d39241aab7c4bbeb07c6d516421c67d">00896</a>                         vec <a class="code" href="classbdm_1_1eEmp.html#9d39241aab7c4bbeb07c6d516421c67d" title="Sample weights .">w</a>;
+<a name="l00898"></a><a class="code" href="classbdm_1_1eEmp.html#73d819553a0f268b055a087d2d4486f3">00898</a>                         Array&lt;vec&gt; <a class="code" href="classbdm_1_1eEmp.html#73d819553a0f268b055a087d2d4486f3" title="Samples .">samples</a>;
+<a name="l00899"></a>00899                 <span class="keyword">public</span>:
+<a name="l00902"></a>00902                         <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="l00903"></a>00903                         <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="l00905"></a>00905 
+<a name="l00907"></a>00907                         <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1eEmp.html#7cfd383180b486fe4526bdf0179350c0" title="Set samples and weights.">set_statistics</a> ( <span class="keyword">const</span> vec &amp;w0, <span class="keyword">const</span> epdf* pdf0 );
+<a name="l00909"></a><a class="code" href="classbdm_1_1eEmp.html#cef74aa5f87d10d440b9b1e8bc78c1e7">00909</a>                         <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1eEmp.html#cef74aa5f87d10d440b9b1e8bc78c1e7" title="Set samples and weights.">set_statistics</a> ( <span class="keyword">const</span> <a class="code" href="classbdm_1_1epdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a>* pdf0 , <span class="keywordtype">int</span> <a class="code" href="classbdm_1_1eEmp.html#9798006271ca77629855113f1283a031" title="Number of particles.">n</a> ) {<a class="code" href="classbdm_1_1eEmp.html#cef74aa5f87d10d440b9b1e8bc78c1e7" title="Set samples and weights.">set_statistics</a> ( ones ( n ) /n,pdf0 );};
+<a name="l00911"></a>00911                         <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="l00913"></a><a class="code" href="classbdm_1_1eEmp.html#c74c281d652356c19b6b079e42ca7ef1">00913</a>                         <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1eEmp.html#c74c281d652356c19b6b079e42ca7ef1" title="Set sample.">set_parameters</a> ( <span class="keywordtype">int</span> n0, <span class="keywordtype">bool</span> copy=<span class="keyword">true</span> ) {<a class="code" href="classbdm_1_1eEmp.html#9798006271ca77629855113f1283a031" title="Number of particles.">n</a>=n0; <a class="code" href="classbdm_1_1eEmp.html#9d39241aab7c4bbeb07c6d516421c67d" title="Sample weights .">w</a>.set_size ( n0,copy );<a class="code" href="classbdm_1_1eEmp.html#73d819553a0f268b055a087d2d4486f3" title="Samples .">samples</a>.set_size ( n0,copy );};
+<a name="l00915"></a><a class="code" href="classbdm_1_1eEmp.html#d7f83cc0415cd44ae7cc8b4bdad93aef">00915</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="l00917"></a><a class="code" href="classbdm_1_1eEmp.html#b7d7106f486e3fad38590914a693d714">00917</a>                         <span class="keyword">const</span> vec&amp; <a class="code" href="classbdm_1_1eEmp.html#b7d7106f486e3fad38590914a693d714" title="Potentially dangerous, use with care.">_w</a>()<span class="keyword"> const </span>{<span class="keywordflow">return</span> <a class="code" href="classbdm_1_1eEmp.html#9d39241aab7c4bbeb07c6d516421c67d" title="Sample weights .">w</a>;};
+<a name="l00919"></a><a class="code" href="classbdm_1_1eEmp.html#c24966b0aaeb767bc8a6b4fd60931be2">00919</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="l00921"></a><a class="code" href="classbdm_1_1eEmp.html#b59af0efdb009d98ea8ebfa965e74ae2">00921</a>                         <span class="keyword">const</span> Array&lt;vec&gt;&amp; <a class="code" href="classbdm_1_1eEmp.html#b59af0efdb009d98ea8ebfa965e74ae2" title="access function">_samples</a>()<span class="keyword"> const </span>{<span class="keywordflow">return</span> <a class="code" href="classbdm_1_1eEmp.html#73d819553a0f268b055a087d2d4486f3" title="Samples .">samples</a>;};
+<a name="l00923"></a>00923                         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="l00925"></a><a class="code" href="classbdm_1_1eEmp.html#97f1e07b5ae6eebc91c7365f0f88d270">00925</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="l00927"></a><a class="code" href="classbdm_1_1eEmp.html#01654c014d3aa068f8d4ecba4be86d09">00927</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="l00928"></a><a class="code" href="classbdm_1_1eEmp.html#bbfcb4f868c7381298c281a256d8c4b9">00928</a>                         vec <a class="code" href="classbdm_1_1eEmp.html#bbfcb4f868c7381298c281a256d8c4b9" title="return expected value">mean</a>()<span class="keyword"> const</span>
+<a name="l00929"></a>00929 <span class="keyword">                        </span>{
+<a name="l00930"></a>00930                                 vec pom=zeros ( <a class="code" href="classbdm_1_1epdf.html#16adac20ec7fe07e1ea0b27d917788ce" title="dimension of the random variable">dim</a> );
+<a name="l00931"></a>00931                                 <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="l00932"></a>00932                                 <span class="keywordflow">return</span> pom;
+<a name="l00933"></a>00933                         }
+<a name="l00934"></a><a class="code" href="classbdm_1_1eEmp.html#05e9ebf467ede737cb6a3621d7fd3c87">00934</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="l00935"></a>00935 <span class="keyword">                        </span>{
+<a name="l00936"></a>00936                                 vec pom=zeros ( <a class="code" href="classbdm_1_1epdf.html#16adac20ec7fe07e1ea0b27d917788ce" title="dimension of the random variable">dim</a> );
+<a name="l00937"></a>00937                                 <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="l00938"></a>00938                                 <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="l00939"></a>00939                         }
+<a name="l00941"></a><a class="code" href="classbdm_1_1eEmp.html#b1c9df656144edf79ba2d885613f661f">00941</a>                         <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1eEmp.html#b1c9df656144edf79ba2d885613f661f" title="For this class, qbounds are minimum and maximum value of the population!">qbounds</a> ( vec &amp;lb, vec &amp;ub, <span class="keywordtype">double</span> perc=0.95 )<span class="keyword"> const</span>
+<a name="l00942"></a>00942 <span class="keyword">                        </span>{
+<a name="l00943"></a>00943                                 <span class="comment">// lb in inf so than it will be pushed below;</span>
+<a name="l00944"></a>00944                                 lb.set_size ( <a class="code" href="classbdm_1_1epdf.html#16adac20ec7fe07e1ea0b27d917788ce" title="dimension of the random variable">dim</a> );
+<a name="l00945"></a>00945                                 ub.set_size ( <a class="code" href="classbdm_1_1epdf.html#16adac20ec7fe07e1ea0b27d917788ce" title="dimension of the random variable">dim</a> );
+<a name="l00946"></a>00946                                 lb = std::numeric_limits&lt;double&gt;::infinity();
+<a name="l00947"></a>00947                                 ub = -std::numeric_limits&lt;double&gt;::infinity();
+<a name="l00948"></a>00948                                 <span class="keywordtype">int</span> j;
+<a name="l00949"></a>00949                                 <span class="keywordflow">for</span> ( <span class="keywordtype">int</span> i=0;i&lt;<a class="code" href="classbdm_1_1eEmp.html#9798006271ca77629855113f1283a031" title="Number of particles.">n</a>;i++ )
+<a name="l00950"></a>00950                                 {
+<a name="l00951"></a>00951                                         <span class="keywordflow">for</span> ( j=0;j&lt;<a class="code" href="classbdm_1_1epdf.html#16adac20ec7fe07e1ea0b27d917788ce" title="dimension of the random variable">dim</a>; j++ )
+<a name="l00952"></a>00952                                         {
+<a name="l00953"></a>00953                                                 <span class="keywordflow">if</span> ( <a class="code" href="classbdm_1_1eEmp.html#73d819553a0f268b055a087d2d4486f3" title="Samples .">samples</a> ( i ) ( j ) &lt;lb ( j ) ) {lb ( j ) =<a class="code" href="classbdm_1_1eEmp.html#73d819553a0f268b055a087d2d4486f3" title="Samples .">samples</a> ( i ) ( j );}
+<a name="l00954"></a>00954                                                 <span class="keywordflow">if</span> ( <a class="code" href="classbdm_1_1eEmp.html#73d819553a0f268b055a087d2d4486f3" title="Samples .">samples</a> ( i ) ( j ) &gt;ub ( j ) ) {ub ( j ) =<a class="code" href="classbdm_1_1eEmp.html#73d819553a0f268b055a087d2d4486f3" title="Samples .">samples</a> ( i ) ( j );}
+<a name="l00955"></a>00955                                         }
+<a name="l00956"></a>00956                                 }
+<a name="l00957"></a>00957                         }
+<a name="l00958"></a>00958         };
+<a name="l00959"></a>00959 
+<a name="l00960"></a>00960 
+<a name="l00962"></a>00962 
+<a name="l00963"></a>00963         <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt;
+<a name="l00964"></a>00964         <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="l00965"></a>00965         {
+<a name="l00966"></a>00966 <span class="comment">//Fixme test dimensions of mu0 and R0;</span>
+<a name="l00967"></a>00967                 mu = mu0;
+<a name="l00968"></a>00968                 R = R0;
+<a name="l00969"></a>00969                 <a class="code" href="classbdm_1_1epdf.html#16adac20ec7fe07e1ea0b27d917788ce" title="dimension of the random variable">dim</a> = mu0.length();
+<a name="l00970"></a>00970         };
+<a name="l00971"></a>00971 
+<a name="l00972"></a>00972         <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt;
+<a name="l00973"></a><a class="code" href="classbdm_1_1enorm.html#d2e0d3a1e30ab3ab04df2d0c43ae74a2">00973</a>         <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1enorm.html#d2e0d3a1e30ab3ab04df2d0c43ae74a2" title="dupdate in exponential form (not really handy)">enorm&lt;sq_T&gt;::dupdate</a> ( mat &amp;v, <span class="keywordtype">double</span> nu )
+<a name="l00974"></a>00974         {
+<a name="l00975"></a>00975                 <span class="comment">//</span>
+<a name="l00976"></a>00976         };
+<a name="l00977"></a>00977 
+<a name="l00978"></a>00978 <span class="comment">// template&lt;class sq_T&gt;</span>
+<a name="l00979"></a>00979 <span class="comment">// void enorm&lt;sq_T&gt;::tupdate ( double phi, mat &amp;vbar, double nubar ) {</span>
+<a name="l00980"></a>00980 <span class="comment">//      //</span>
+<a name="l00981"></a>00981 <span class="comment">// };</span>
+<a name="l00982"></a>00982 
+<a name="l00983"></a>00983         <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt;
+<a name="l00984"></a><a class="code" href="classbdm_1_1enorm.html#e1a48f52351ec3a349bd443b713b1766">00984</a>         vec <a class="code" href="classbdm_1_1enorm.html#e1a48f52351ec3a349bd443b713b1766" title="Returns a sample,  from density .">enorm&lt;sq_T&gt;::sample</a>()<span class="keyword"> const</span>
+<a name="l00985"></a>00985 <span class="keyword">        </span>{
+<a name="l00986"></a>00986                 vec x ( <a class="code" href="classbdm_1_1epdf.html#16adac20ec7fe07e1ea0b27d917788ce" title="dimension of the random variable">dim</a> );
+<a name="l00987"></a>00987 <span class="preprocessor">#pragma omp critical</span>
+<a name="l00988"></a>00988 <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="l00989"></a>00989                 vec smp = <a class="code" href="classbdm_1_1enorm.html#2d92dde696b2a7a5b10ddef5d22ba2c2" title="Covariance matrix in decomposed form.">R</a>.sqrt_mult ( x );
+<a name="l00990"></a>00990 
+<a name="l00991"></a>00991                 smp += <a class="code" href="classbdm_1_1enorm.html#c702a194720853570d08b65482f842c7" title="mean value">mu</a>;
+<a name="l00992"></a>00992                 <span class="keywordflow">return</span> smp;
+<a name="l00993"></a>00993         };
+<a name="l00994"></a>00994 
+<a name="l00995"></a>00995         <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt;
+<a name="l00996"></a>00996         mat <a class="code" href="classbdm_1_1enorm.html#e1a48f52351ec3a349bd443b713b1766" title="Returns a sample,  from density .">enorm&lt;sq_T&gt;::sample</a> ( <span class="keywordtype">int</span> N )<span class="keyword"> const</span>
+<a name="l00997"></a>00997 <span class="keyword">        </span>{
+<a name="l00998"></a>00998                 mat X ( <a class="code" href="classbdm_1_1epdf.html#16adac20ec7fe07e1ea0b27d917788ce" title="dimension of the random variable">dim</a>,N );
+<a name="l00999"></a>00999                 vec x ( <a class="code" href="classbdm_1_1epdf.html#16adac20ec7fe07e1ea0b27d917788ce" title="dimension of the random variable">dim</a> );
+<a name="l01000"></a>01000                 vec pom;
+<a name="l01001"></a>01001                 <span class="keywordtype">int</span> i;
+<a name="l01002"></a>01002 
+<a name="l01003"></a>01003                 <span class="keywordflow">for</span> ( i=0;i&lt;N;i++ )
+<a name="l01004"></a>01004                 {
+<a name="l01005"></a>01005 <span class="preprocessor">#pragma omp critical</span>
+<a name="l01006"></a>01006 <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="l01007"></a>01007                         pom = <a class="code" href="classbdm_1_1enorm.html#2d92dde696b2a7a5b10ddef5d22ba2c2" title="Covariance matrix in decomposed form.">R</a>.sqrt_mult ( x );
+<a name="l01008"></a>01008                         pom +=<a class="code" href="classbdm_1_1enorm.html#c702a194720853570d08b65482f842c7" title="mean value">mu</a>;
+<a name="l01009"></a>01009                         X.set_col ( i, pom );
+<a name="l01010"></a>01010                 }
+<a name="l01011"></a>01011 
+<a name="l01012"></a>01012                 <span class="keywordflow">return</span> X;
+<a name="l01013"></a>01013         };
+<a name="l01014"></a>01014 
+<a name="l01015"></a>01015 <span class="comment">// template&lt;class sq_T&gt;</span>
+<a name="l01016"></a>01016 <span class="comment">// double enorm&lt;sq_T&gt;::eval ( const vec &amp;val ) const {</span>
+<a name="l01017"></a>01017 <span class="comment">//      double pdfl,e;</span>
+<a name="l01018"></a>01018 <span class="comment">//      pdfl = evallog ( val );</span>
+<a name="l01019"></a>01019 <span class="comment">//      e = exp ( pdfl );</span>
+<a name="l01020"></a>01020 <span class="comment">//      return e;</span>
+<a name="l01021"></a>01021 <span class="comment">// };</span>
+<a name="l01022"></a>01022 
+<a name="l01023"></a>01023         <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt;
+<a name="l01024"></a><a class="code" href="classbdm_1_1enorm.html#e13aeed5b543b2179bacdc4fa2ae47a3">01024</a>         <span class="keywordtype">double</span> <a class="code" href="classbdm_1_1enorm.html#e13aeed5b543b2179bacdc4fa2ae47a3" title="Evaluate normalized log-probability.">enorm&lt;sq_T&gt;::evallog_nn</a> ( <span class="keyword">const</span> vec &amp;val )<span class="keyword"> const</span>
+<a name="l01025"></a>01025 <span class="keyword">        </span>{
+<a name="l01026"></a>01026                 <span class="comment">// 1.83787706640935 = log(2pi)</span>
+<a name="l01027"></a>01027                 <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="l01028"></a>01028                 <span class="keywordflow">return</span>  tmp;
+<a name="l01029"></a>01029         };
+<a name="l01030"></a>01030 
+<a name="l01031"></a>01031         <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt;
+<a name="l01032"></a><a class="code" href="classbdm_1_1enorm.html#25785343aff102cc5df1cab08ba16d32">01032</a>         <span class="keyword">inline</span> <span class="keywordtype">double</span> <a class="code" href="classbdm_1_1enorm.html#25785343aff102cc5df1cab08ba16d32" title="logarithm of the normalizing constant, ">enorm&lt;sq_T&gt;::lognc</a> ()<span class="keyword"> const</span>
+<a name="l01033"></a>01033 <span class="keyword">        </span>{
+<a name="l01034"></a>01034                 <span class="comment">// 1.83787706640935 = log(2pi)</span>
+<a name="l01035"></a>01035                 <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="l01036"></a>01036                 <span class="keywordflow">return</span> tmp;
+<a name="l01037"></a>01037         };
+<a name="l01038"></a>01038 
+<a name="l01039"></a>01039         <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt;
+<a name="l01040"></a><a class="code" href="classbdm_1_1mlnorm.html#5d18dec3167584338a4775c1d165d96f">01040</a>         <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1mlnorm.html#5d18dec3167584338a4775c1d165d96f" title="Set A and R.">mlnorm&lt;sq_T&gt;::set_parameters</a> ( <span class="keyword">const</span> mat &amp;A0, <span class="keyword">const</span> vec &amp;mu0, <span class="keyword">const</span> sq_T &amp;R0 )
+<a name="l01041"></a>01041         {
+<a name="l01042"></a>01042                 it_assert_debug ( A0.rows() ==mu0.length(),<span class="stringliteral">""</span> );
+<a name="l01043"></a>01043                 it_assert_debug ( A0.rows() ==R0.rows(),<span class="stringliteral">""</span> );
+<a name="l01044"></a>01044 
+<a name="l01045"></a>01045                 <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="l01046"></a>01046                 A = A0;
+<a name="l01047"></a>01047                 mu_const = mu0;
+<a name="l01048"></a>01048                 <a class="code" href="classbdm_1_1mpdf.html#7c1900976ff13dbc09c9729b3bbff9e6" title="dimension of the condition">dimc</a>=A0.cols();
+<a name="l01049"></a>01049         }
+<a name="l01050"></a>01050 
+<a name="l01051"></a>01051 <span class="comment">// template&lt;class sq_T&gt;</span>
+<a name="l01052"></a>01052 <span class="comment">// vec mlnorm&lt;sq_T&gt;::samplecond (const  vec &amp;cond, double &amp;lik ) {</span>
+<a name="l01053"></a>01053 <span class="comment">//      this-&gt;condition ( cond );</span>
+<a name="l01054"></a>01054 <span class="comment">//      vec smp = epdf.sample();</span>
+<a name="l01055"></a>01055 <span class="comment">//      lik = epdf.eval ( smp );</span>
+<a name="l01056"></a>01056 <span class="comment">//      return smp;</span>
+<a name="l01057"></a>01057 <span class="comment">// }</span>
+<a name="l01058"></a>01058 
+<a name="l01059"></a>01059 <span class="comment">// template&lt;class sq_T&gt;</span>
+<a name="l01060"></a>01060 <span class="comment">// mat mlnorm&lt;sq_T&gt;::samplecond (const vec &amp;cond, vec &amp;lik, int n ) {</span>
+<a name="l01061"></a>01061 <span class="comment">//      int i;</span>
+<a name="l01062"></a>01062 <span class="comment">//      int dim = rv.count();</span>
+<a name="l01063"></a>01063 <span class="comment">//      mat Smp ( dim,n );</span>
+<a name="l01064"></a>01064 <span class="comment">//      vec smp ( dim );</span>
+<a name="l01065"></a>01065 <span class="comment">//      this-&gt;condition ( cond );</span>
+<a name="l01066"></a>01066 <span class="comment">//</span>
+<a name="l01067"></a>01067 <span class="comment">//      for ( i=0; i&lt;n; i++ ) {</span>
+<a name="l01068"></a>01068 <span class="comment">//              smp = epdf.sample();</span>
+<a name="l01069"></a>01069 <span class="comment">//              lik ( i ) = epdf.eval ( smp );</span>
+<a name="l01070"></a>01070 <span class="comment">//              Smp.set_col ( i ,smp );</span>
+<a name="l01071"></a>01071 <span class="comment">//      }</span>
+<a name="l01072"></a>01072 <span class="comment">//</span>
+<a name="l01073"></a>01073 <span class="comment">//      return Smp;</span>
+<a name="l01074"></a>01074 <span class="comment">// }</span>
+<a name="l01075"></a>01075 
+<a name="l01076"></a>01076         <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt;
+<a name="l01077"></a><a class="code" href="classbdm_1_1mlnorm.html#0dafc0196e7e07fd06dc6716e0e18bc2">01077</a>         <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1mlnorm.html#0dafc0196e7e07fd06dc6716e0e18bc2">mlnorm&lt;sq_T&gt;::condition</a> ( <span class="keyword">const</span> vec &amp;cond )
+<a name="l01078"></a>01078         {
+<a name="l01079"></a>01079                 _mu = A*cond + mu_const;
+<a name="l01080"></a>01080 <span class="comment">//R is already assigned;</span>
+<a name="l01081"></a>01081         }
+<a name="l01082"></a>01082 
+<a name="l01083"></a>01083         <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt;
+<a name="l01084"></a><a class="code" href="classbdm_1_1enorm.html#cd02d76e9d4f96bdd3fa6b604e273039">01084</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#cd02d76e9d4f96bdd3fa6b604e273039" title="Return marginal density on the given RV, the remainig rvs are intergrated out.">enorm&lt;sq_T&gt;::marginal</a> ( <span class="keyword">const</span> <a class="code" href="classbdm_1_1RV.html" title="Class representing variables, most often random variables.">RV</a> &amp;rvn )<span class="keyword"> const</span>
+<a name="l01085"></a>01085 <span class="keyword">        </span>{
+<a name="l01086"></a>01086                 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="l01087"></a>01087                 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="l01088"></a>01088 
+<a name="l01089"></a>01089                 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="l01090"></a>01090 
+<a name="l01091"></a>01091                 <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="l01092"></a>01092                 tmp-&gt;<a class="code" href="classbdm_1_1epdf.html#f423e28448dbb69ef4905295ec8de8ff" title="Name its rv.">set_rv</a> ( rvn );
+<a name="l01093"></a>01093                 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="l01094"></a>01094                 <span class="keywordflow">return</span> tmp;
+<a name="l01095"></a>01095         }
+<a name="l01096"></a>01096 
+<a name="l01097"></a>01097         <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt;
+<a name="l01098"></a><a class="code" href="classbdm_1_1enorm.html#baea4d49c657342b58297d68cda16d26">01098</a>         <a class="code" href="classbdm_1_1mpdf.html" title="Conditional probability density, e.g. modeling some dependencies.">mpdf</a>* <a class="code" href="classbdm_1_1enorm.html#baea4d49c657342b58297d68cda16d26" title="Return conditional density on the given RV, the remaining rvs will be in conditioning...">enorm&lt;sq_T&gt;::condition</a> ( <span class="keyword">const</span> <a class="code" href="classbdm_1_1RV.html" title="Class representing variables, most often random variables.">RV</a> &amp;rvn )<span class="keyword"> const</span>
+<a name="l01099"></a>01099 <span class="keyword">        </span>{
+<a name="l01100"></a>01100 
+<a name="l01101"></a>01101                 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="l01102"></a>01102 
+<a name="l01103"></a>01103                 <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="l01104"></a>01104                 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="l01105"></a>01105                 <span class="comment">//Permutation vector of the new R</span>
+<a name="l01106"></a>01106                 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="l01107"></a>01107                 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="l01108"></a>01108                 ivec perm=concat ( irvn , irvc );
+<a name="l01109"></a>01109                 sq_T Rn ( <a class="code" href="classbdm_1_1enorm.html#2d92dde696b2a7a5b10ddef5d22ba2c2" title="Covariance matrix in decomposed form.">R</a>,perm );
+<a name="l01110"></a>01110 
+<a name="l01111"></a>01111                 <span class="comment">//fixme - could this be done in general for all sq_T?</span>
+<a name="l01112"></a>01112                 mat S=Rn.to_mat();
+<a name="l01113"></a>01113                 <span class="comment">//fixme</span>
+<a name="l01114"></a>01114                 <span class="keywordtype">int</span> n=rvn.<a class="code" href="classbdm_1_1RV.html#de30156104f61d86c94f758861418089">_dsize</a>()-1;
+<a name="l01115"></a>01115                 <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="l01116"></a>01116                 mat S11 = S.get ( 0,n, 0, n );
+<a name="l01117"></a>01117                 mat S12 = S.get ( 0, n , rvn.<a class="code" href="classbdm_1_1RV.html#de30156104f61d86c94f758861418089">_dsize</a>(), end );
+<a name="l01118"></a>01118                 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="l01119"></a>01119 
+<a name="l01120"></a>01120                 vec mu1 = <a class="code" href="classbdm_1_1enorm.html#c702a194720853570d08b65482f842c7" title="mean value">mu</a> ( irvn );
+<a name="l01121"></a>01121                 vec mu2 = <a class="code" href="classbdm_1_1enorm.html#c702a194720853570d08b65482f842c7" title="mean value">mu</a> ( irvc );
+<a name="l01122"></a>01122                 mat A=S12*inv ( S22 );
+<a name="l01123"></a>01123                 sq_T R_n ( S11 - A *S12.T() );
+<a name="l01124"></a>01124 
+<a name="l01125"></a>01125                 <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="l01126"></a>01126                 tmp-&gt;set_rv ( rvn ); tmp-&gt;set_rvc ( rvc );
+<a name="l01127"></a>01127                 tmp-&gt;set_parameters ( A,mu1-A*mu2,R_n );
+<a name="l01128"></a>01128                 <span class="keywordflow">return</span> tmp;
+<a name="l01129"></a>01129         }
+<a name="l01130"></a>01130 
+<a name="l01132"></a>01132 
+<a name="l01133"></a>01133         <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt;
+<a name="l01134"></a>01134         std::ostream &amp;operator&lt;&lt; ( std::ostream &amp;os,  mlnorm&lt;sq_T&gt; &amp;ml )
+<a name="l01135"></a>01135         {
+<a name="l01136"></a>01136                 os &lt;&lt; <span class="stringliteral">"A:"</span>&lt;&lt; ml.A&lt;&lt;endl;
+<a name="l01137"></a>01137                 os &lt;&lt; <span class="stringliteral">"mu:"</span>&lt;&lt; ml.mu_const&lt;&lt;endl;
+<a name="l01138"></a>01138                 os &lt;&lt; <span class="stringliteral">"R:"</span> &lt;&lt; ml.epdf._R().to_mat() &lt;&lt;endl;
+<a name="l01139"></a>01139                 <span class="keywordflow">return</span> os;
+<a name="l01140"></a>01140         };
+<a name="l01141"></a>01141 
+<a name="l01142"></a>01142 }
+<a name="l01143"></a>01143 <span class="preprocessor">#endif //EF_H</span>
 </pre></div></div>
-<hr size="1"><address style="text-align: right;"><small>Generated on Fri Mar 6 15:01:36 2009 for mixpp by&nbsp;
+<hr size="1"><address style="text-align: right;"><small>Generated on Tue Mar 17 13:51:48 2009 for mixpp by&nbsp;
 <a href="http://www.doxygen.org/index.html">
 <img src="doxygen.png" alt="doxygen" align="middle" border="0"></a> 1.5.8 </small></address>