Changeset 270 for doc/html/libEF_8h-source.html

Timestamp:

02/16/09 10:02:08 (15 years ago)

Author:

smidl

Message:

Changes in the very root classes!
* rv and rvc are no longer compulsory,
* samplecond does not return ll
* BM has drv

Files:

• doc/html/libEF_8h-source.html (modified) (7 diffs)

doc/html/libEF_8h-source.html

@@ -26 +26 @@
 <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="l00021"></a>00021 <span class="keyword">namespace </span>bdm {
 <a name="l00022"></a>00022 
 <a name="l00023"></a>00023 
@@ -36 +36 @@
 <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#1e92e3f94e594edb20adfa81ae9e2959">00041</a> <span class="comment"></span>        <a class="code" href="classbdm_1_1eEF.html#1e92e3f94e594edb20adfa81ae9e2959" title="default constructor">eEF</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="Identified of the random variable.">rv</a> ) :<a class="code" href="classbdm_1_1epdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a> ( rv ) {};
+<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="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#a36d06ecdd6f4c79dc122510eaccc692" title="Evaluate normalized log-probability.">evallog</a> ( <span class="keyword">const</span> mat &amp;Val )<span class="keyword"> const </span>{
 <a name="l00052"></a>00052                 vec x ( Val.cols() );
@@ -52 +52 @@
 <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#f6647b16e9c99b8a7d7df93374ef90f3">00070</a>         <a class="code" href="classbdm_1_1mEF.html#f6647b16e9c99b8a7d7df93374ef90f3" title="Default constructor.">mEF</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;rv0, <span class="keyword">const</span> <a class="code" href="classbdm_1_1RV.html" title="Class representing variables, most often random variables.">RV</a> &amp;rvc0 ) :<a class="code" href="classbdm_1_1mpdf.html" title="Conditional probability density, e.g. modeling some dependencies.">mpdf</a> ( rv0,rvc0 ) {};
+<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 
@@ -60 +60 @@
 <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#73bccd1d8142d4d330e35637ca30decc">00082</a>         <a class="code" href="classbdm_1_1BMEF.html#73bccd1d8142d4d330e35637ca30decc" title="Default constructor.">BMEF</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_1BM.html#18d6db4af8ee42077741d9e3618153ca" title="Random variable of the posterior.">rv</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> ( rv ), <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#73bccd1d8142d4d330e35637ca30decc" title="Default 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="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#2def512872ed8a4fc3b702371ec0be55" title="Default constructor (=empty 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 ) {};
@@ -69 +69 @@
 <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#5912dbcf28ae711e30b08c2fa766a3e6">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#5912dbcf28ae711e30b08c2fa766a3e6" title="Flatten the posterior as if to keep nu0 data.">_copy_</a> ( <span class="keywordtype">bool</span> changerv=<span class="keyword">false</span> ) {it_error ( <span class="stringliteral">"function _copy_ not implemented for this BM"</span> ); <span class="keywordflow">return</span> NULL;};
+<a name="l00096"></a><a class="code" href="classbdm_1_1BMEF.html#5912dbcf28ae711e30b08c2fa766a3e6">00096</a>         <a class="code" href="classbdm_1_1BMEF.html" title="Estimator for Exponential family.">BMEF</a>* <a class="code" href="classbdm_1_1BM.html#c0f027ff91d8459937c6f60ff8e553ff">_copy_</a> ( <span class="keywordtype">bool</span> changerv=<span class="keyword">false</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 
@@ -80 +80 @@
 <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="l00115"></a><a class="code" href="classbdm_1_1enorm.html#91a2d4a91364b0144e1523cad4d1135b">00115</a>         <span class="keywordtype">int</span> <a class="code" href="classbdm_1_1enorm.html#91a2d4a91364b0144e1523cad4d1135b" title="dimension (redundant from rv.count() for easier coding )">dim</a>;
-<a name="l00116"></a>00116 <span class="keyword">public</span>:
-<a name="l00118"></a>00118         <a class="code" href="classbdm_1_1enorm.html#7d433390d6bbad337986945b63d7fbe9" title="Default constructor.">enorm</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="Identified of the random variable.">rv</a> );
-<a name="l00120"></a>00120         <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1enorm.html#b8322f2c11560871dd922c660f4771bb" title="Set mean value mu and covariance R.">set_parameters</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> );
-<a name="l00122"></a>00122         <span class="comment">//void tupdate ( double phi, mat &amp;vbar, double nubar );</span>
-<a name="l00124"></a>00124 <span class="comment"></span>        <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="l00125"></a>00125 
-<a name="l00126"></a>00126         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="l00128"></a>00128         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="l00129"></a>00129 <span class="comment">//      double eval ( const vec &amp;val ) const ;</span>
-<a name="l00130"></a>00130         <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="l00131"></a>00131         <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="l00132"></a><a class="code" href="classbdm_1_1enorm.html#b2fa2915c35366392fe9bb022ca1a600">00132</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="l00133"></a><a class="code" href="classbdm_1_1enorm.html#729c75ef0fa8abae03d58ad1f81e6773">00133</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="l00134"></a>00134 <span class="comment">//      mlnorm&lt;sq_T&gt;* condition ( const RV &amp;rvn ) const ;</span>
-<a name="l00135"></a>00135         <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="l00136"></a>00136 <span class="comment">//      enorm&lt;sq_T&gt;* marginal ( const RV &amp;rv ) const;</span>
-<a name="l00137"></a>00137         <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="Identified of the random variable.">rv</a> ) <span class="keyword">const</span>;
-<a name="l00138"></a>00138 <span class="comment">//Access methods</span>
-<a name="l00140"></a><a class="code" href="classbdm_1_1enorm.html#766127847e9482aea9226ea157295ea2">00140</a> <span class="comment"></span>        vec&amp; <a class="code" href="classbdm_1_1enorm.html#766127847e9482aea9226ea157295ea2" title="returns a pointer to the internal mean value. Use with Care!">_mu</a>() {<span class="keywordflow">return</span> <a class="code" href="classbdm_1_1enorm.html#c702a194720853570d08b65482f842c7" title="mean value">mu</a>;}
-<a name="l00141"></a>00141 
-<a name="l00143"></a><a class="code" href="classbdm_1_1enorm.html#8915d68ae76ad185c8c314f960a63f0c">00143</a>         <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1enorm.html#8915d68ae76ad185c8c314f960a63f0c" title="access function">set_mu</a> ( <span class="keyword">const</span> vec mu0 ) { <a class="code" href="classbdm_1_1enorm.html#c702a194720853570d08b65482f842c7" title="mean value">mu</a>=mu0;}
-<a name="l00144"></a>00144 
-<a name="l00146"></a><a class="code" href="classbdm_1_1enorm.html#81d81e35e57c9f194bde248e3affcf1f">00146</a>         sq_T&amp; <a class="code" href="classbdm_1_1enorm.html#81d81e35e57c9f194bde248e3affcf1f" title="returns pointers to the internal variance and its inverse. Use with Care!">_R</a>() {<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; <a class="code" href="classbdm_1_1enorm.html#81d81e35e57c9f194bde248e3affcf1f" title="returns pointers to the internal variance and its inverse. Use with Care!">_R</a>()<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="l00148"></a>00148 
-<a name="l00150"></a>00150 <span class="comment">//      mat getR () {return R.to_mat();}</span>
-<a name="l00151"></a>00151 };
-<a name="l00152"></a>00152 
-<a name="l00159"></a><a class="code" href="classbdm_1_1egiw.html">00159</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="l00160"></a>00160 <span class="keyword">protected</span>:
-<a name="l00162"></a><a class="code" href="classbdm_1_1egiw.html#ae56852845c6af176fd9017dbebbbd52">00162</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="l00164"></a><a class="code" href="classbdm_1_1egiw.html#447eacf19d4f4083872686f044814dc4">00164</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="l00166"></a><a class="code" href="classbdm_1_1egiw.html#40b68a9c3b2120fba94cc4d2fcd291e1">00166</a>         <span class="keywordtype">int</span> <a class="code" href="classbdm_1_1egiw.html#40b68a9c3b2120fba94cc4d2fcd291e1" title="Dimension of the output.">xdim</a>;
-<a name="l00168"></a><a class="code" href="classbdm_1_1egiw.html#322414c32d9a21a006a5aab0311f64fd">00168</a>         <span class="keywordtype">int</span> <a class="code" href="classbdm_1_1egiw.html#322414c32d9a21a006a5aab0311f64fd" title="Dimension of the regressor.">nPsi</a>;
-<a name="l00169"></a>00169 <span class="keyword">public</span>:
-<a name="l00171"></a><a class="code" href="classbdm_1_1egiw.html#a60e072c191acf65ab480deeb11c5b88">00171</a>         <a class="code" href="classbdm_1_1egiw.html#a60e072c191acf65ab480deeb11c5b88" title="Default constructor, if nu0&amp;lt;0 a minimal nu0 will be computed.">egiw</a> ( <a class="code" href="classbdm_1_1RV.html" title="Class representing variables, most often random variables.">RV</a> <a class="code" href="classbdm_1_1epdf.html#62c5b8ff71d9ebe6cd58d3c342eb1dc8" title="Identified of the random variable.">rv</a>, mat 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> ( rv ), <a class="code" href="classbdm_1_1egiw.html#ae56852845c6af176fd9017dbebbbd52" title="Extended information matrix of sufficient statistics.">V</a> ( V0 ), <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="l00172"></a>00172                 <a class="code" href="classbdm_1_1egiw.html#40b68a9c3b2120fba94cc4d2fcd291e1" title="Dimension of the output.">xdim</a> = rv.<a class="code" href="classbdm_1_1RV.html#2174751a00ce19f941edd2c1a861be67" title="Return number of scalars in the RV.">count</a>() /<a class="code" href="classbdm_1_1egiw.html#ae56852845c6af176fd9017dbebbbd52" title="Extended information matrix of sufficient statistics.">V</a>.<a class="code" href="group__math.html#g96dfb21865db4f5bd36fa70f9b0b1163" title="access function">rows</a>();
-<a name="l00173"></a>00173                 it_assert_debug ( rv.<a class="code" href="classbdm_1_1RV.html#2174751a00ce19f941edd2c1a861be67" title="Return number of scalars in the RV.">count</a>() ==<a class="code" href="classbdm_1_1egiw.html#40b68a9c3b2120fba94cc4d2fcd291e1" title="Dimension of the output.">xdim</a>*<a class="code" href="classbdm_1_1egiw.html#ae56852845c6af176fd9017dbebbbd52" title="Extended information matrix of sufficient statistics.">V</a>.<a class="code" href="group__math.html#g96dfb21865db4f5bd36fa70f9b0b1163" title="access function">rows</a>(),<span class="stringliteral">"Incompatible V0."</span> );
-<a name="l00174"></a>00174                 <a class="code" href="classbdm_1_1egiw.html#322414c32d9a21a006a5aab0311f64fd" title="Dimension of the regressor.">nPsi</a> = <a class="code" href="classbdm_1_1egiw.html#ae56852845c6af176fd9017dbebbbd52" title="Extended information matrix of sufficient statistics.">V</a>.<a class="code" href="group__math.html#g96dfb21865db4f5bd36fa70f9b0b1163" title="access function">rows</a>()-<a class="code" href="classbdm_1_1egiw.html#40b68a9c3b2120fba94cc4d2fcd291e1" title="Dimension of the output.">xdim</a>;
-<a name="l00175"></a>00175                 <span class="comment">//set mu to have proper normalization and </span>
-<a name="l00176"></a>00176                 <span class="keywordflow">if</span> (nu0&lt;0){
-<a name="l00177"></a>00177                         <a class="code" href="classbdm_1_1egiw.html#447eacf19d4f4083872686f044814dc4" title="Number of data records (degrees of freedom) of sufficient statistics.">nu</a> = 0.1 +nPsi +2*<a class="code" href="classbdm_1_1egiw.html#40b68a9c3b2120fba94cc4d2fcd291e1" title="Dimension of the output.">xdim</a> +2; <span class="comment">// +2 assures finite expected value of R</span>
-<a name="l00178"></a>00178                         <span class="comment">// terms before that are sufficient for finite normalization</span>
-<a name="l00179"></a>00179                 }
-<a name="l00180"></a>00180         }
-<a name="l00182"></a><a class="code" href="classbdm_1_1egiw.html#bc3db93cb60dd29187eb3c6cfd557f97">00182</a>         <a class="code" href="classbdm_1_1egiw.html#a60e072c191acf65ab480deeb11c5b88" title="Default constructor, if nu0&amp;lt;0 a minimal nu0 will be computed.">egiw</a> ( <a class="code" href="classbdm_1_1RV.html" title="Class representing variables, most often random variables.">RV</a> <a class="code" href="classbdm_1_1epdf.html#62c5b8ff71d9ebe6cd58d3c342eb1dc8" title="Identified of the random variable.">rv</a>, <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> ( rv ), <a class="code" href="classbdm_1_1egiw.html#ae56852845c6af176fd9017dbebbbd52" title="Extended information matrix of sufficient statistics.">V</a> ( V0 ), <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="l00183"></a>00183                 <a class="code" href="classbdm_1_1egiw.html#40b68a9c3b2120fba94cc4d2fcd291e1" title="Dimension of the output.">xdim</a> = rv.<a class="code" href="classbdm_1_1RV.html#2174751a00ce19f941edd2c1a861be67" title="Return number of scalars in the RV.">count</a>() /<a class="code" href="classbdm_1_1egiw.html#ae56852845c6af176fd9017dbebbbd52" title="Extended information matrix of sufficient statistics.">V</a>.<a class="code" href="group__math.html#g96dfb21865db4f5bd36fa70f9b0b1163" title="access function">rows</a>();
-<a name="l00184"></a>00184                 it_assert_debug ( rv.<a class="code" href="classbdm_1_1RV.html#2174751a00ce19f941edd2c1a861be67" title="Return number of scalars in the RV.">count</a>() ==<a class="code" href="classbdm_1_1egiw.html#40b68a9c3b2120fba94cc4d2fcd291e1" title="Dimension of the output.">xdim</a>*<a class="code" href="classbdm_1_1egiw.html#ae56852845c6af176fd9017dbebbbd52" title="Extended information matrix of sufficient statistics.">V</a>.<a class="code" href="group__math.html#g96dfb21865db4f5bd36fa70f9b0b1163" title="access function">rows</a>(),<span class="stringliteral">"Incompatible V0."</span> );
-<a name="l00185"></a>00185                 <a class="code" href="classbdm_1_1egiw.html#322414c32d9a21a006a5aab0311f64fd" title="Dimension of the regressor.">nPsi</a> = <a class="code" href="classbdm_1_1egiw.html#ae56852845c6af176fd9017dbebbbd52" title="Extended information matrix of sufficient statistics.">V</a>.<a class="code" href="group__math.html#g96dfb21865db4f5bd36fa70f9b0b1163" title="access function">rows</a>()-<a class="code" href="classbdm_1_1egiw.html#40b68a9c3b2120fba94cc4d2fcd291e1" title="Dimension of the output.">xdim</a>;
-<a name="l00186"></a>00186                 <span class="keywordflow">if</span> (nu0&lt;0){
-<a name="l00187"></a>00187                         <a class="code" href="classbdm_1_1egiw.html#447eacf19d4f4083872686f044814dc4" title="Number of data records (degrees of freedom) of sufficient statistics.">nu</a> = 0.1 +nPsi +2*<a class="code" href="classbdm_1_1egiw.html#40b68a9c3b2120fba94cc4d2fcd291e1" title="Dimension of the output.">xdim</a> +2; <span class="comment">// +2 assures finite expected value of R</span>
-<a name="l00188"></a>00188                         <span class="comment">// terms before that are sufficient for finite normalization</span>
-<a name="l00189"></a>00189                 }
-<a name="l00190"></a>00190         }
-<a name="l00191"></a>00191 
-<a name="l00192"></a>00192         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="l00193"></a>00193         vec <a class="code" href="classbdm_1_1egiw.html#df70c05f918c3a6f86d60f10c1fd6ba2" title="return expected value">mean</a>() <span class="keyword">const</span>;
-<a name="l00194"></a>00194         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="l00195"></a>00195         <span class="keywordtype">void</span> mean_mat ( mat &amp;M, mat&amp;R ) <span class="keyword">const</span>;
-<a name="l00197"></a>00197         <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="l00198"></a>00198         <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="l00199"></a>00199 
-<a name="l00200"></a>00200         <span class="comment">//Access</span>
-<a name="l00202"></a><a class="code" href="classbdm_1_1egiw.html#15792f3112e5cf67d572f491b09324c8">00202</a> <span class="comment"></span>        <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_1egiw.html#15792f3112e5cf67d572f491b09324c8" title="returns a pointer to the internal statistics. Use with Care!">_V</a>() {<span class="keywordflow">return</span> <a class="code" href="classbdm_1_1egiw.html#ae56852845c6af176fd9017dbebbbd52" title="Extended information matrix of sufficient statistics.">V</a>;}
-<a name="l00204"></a><a class="code" href="classbdm_1_1egiw.html#ad9c539a80a552e837245ddcebcbbba4">00204</a>         <span class="keyword">const</span> <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_1egiw.html#15792f3112e5cf67d572f491b09324c8" title="returns a pointer to the internal statistics. Use with Care!">_V</a>()<span class="keyword"> const </span>{<span class="keywordflow">return</span> <a class="code" href="classbdm_1_1egiw.html#ae56852845c6af176fd9017dbebbbd52" title="Extended information matrix of sufficient statistics.">V</a>;}
-<a name="l00206"></a><a class="code" href="classbdm_1_1egiw.html#a025ee710274ca142dd0ae978735ad4a">00206</a>         <span class="keywordtype">double</span>&amp; <a class="code" href="classbdm_1_1egiw.html#a025ee710274ca142dd0ae978735ad4a" title="returns a pointer to the internal statistics. Use with Care!">_nu</a>()  {<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         <span class="keyword">const</span> <span class="keywordtype">double</span>&amp; <a class="code" href="classbdm_1_1egiw.html#a025ee710274ca142dd0ae978735ad4a" title="returns a pointer to the internal statistics. Use with Care!">_nu</a>()<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="l00208"></a><a class="code" href="classbdm_1_1egiw.html#8e610e95401a11baf34f65e16ecd87be">00208</a>         <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1egiw.html#8e610e95401a11baf34f65e16ecd87be" title="Power of the density, used e.g. to flatten the density.">pow</a> ( <span class="keywordtype">double</span> p ) {<a class="code" href="classbdm_1_1egiw.html#ae56852845c6af176fd9017dbebbbd52" title="Extended information matrix of sufficient statistics.">V</a>*=p;<a class="code" href="classbdm_1_1egiw.html#447eacf19d4f4083872686f044814dc4" title="Number of data records (degrees of freedom) of sufficient statistics.">nu</a>*=p;};
-<a name="l00209"></a>00209 };
-<a name="l00210"></a>00210 
-<a name="l00219"></a><a class="code" href="classbdm_1_1eDirich.html">00219</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="l00220"></a>00220 <span class="keyword">protected</span>:
-<a name="l00222"></a><a class="code" href="classbdm_1_1eDirich.html#f25886a49b4667af61245de81c83b5d2">00222</a>         vec <a class="code" href="classbdm_1_1eDirich.html#f25886a49b4667af61245de81c83b5d2" title="sufficient statistics">beta</a>;
-<a name="l00224"></a><a class="code" href="classbdm_1_1eDirich.html#ee9db192a6f0ab7b29c33b2a18a5e1b4">00224</a>         <span class="keywordtype">double</span> <a class="code" href="classbdm_1_1eDirich.html#ee9db192a6f0ab7b29c33b2a18a5e1b4" title="speedup variable">gamma</a>;
-<a name="l00225"></a>00225 <span class="keyword">public</span>:
-<a name="l00227"></a><a class="code" href="classbdm_1_1eDirich.html#2ae893fe9167f67bca09bc159acbf957">00227</a>         <a class="code" href="classbdm_1_1eDirich.html#2ae893fe9167f67bca09bc159acbf957" title="Default constructor.">eDirich</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="Identified of the random variable.">rv</a>, <span class="keyword">const</span> vec &amp;beta0 ) : <a class="code" href="classbdm_1_1eEF.html" title="General conjugate exponential family posterior density.">eEF</a> ( rv ),<a class="code" href="classbdm_1_1eDirich.html#f25886a49b4667af61245de81c83b5d2" title="sufficient statistics">beta</a> ( beta0 ) {it_assert_debug ( rv.<a class="code" href="classbdm_1_1RV.html#2174751a00ce19f941edd2c1a861be67" title="Return number of scalars in the RV.">count</a>() ==<a class="code" href="classbdm_1_1eDirich.html#f25886a49b4667af61245de81c83b5d2" title="sufficient statistics">beta</a>.length(),<span class="stringliteral">"Incompatible statistics"</span> ); };
-<a name="l00229"></a><a class="code" href="classbdm_1_1eDirich.html#31cc8bf709552c9e7286ac16b27c8e2c">00229</a>         <a class="code" href="classbdm_1_1eDirich.html#2ae893fe9167f67bca09bc159acbf957" title="Default constructor.">eDirich</a> ( <span class="keyword">const</span> <a class="code" href="classbdm_1_1eDirich.html" title="Dirichlet posterior density.">eDirich</a> &amp;D0 ) : <a class="code" href="classbdm_1_1eEF.html" title="General conjugate exponential family posterior density.">eEF</a> ( D0.<a class="code" href="classbdm_1_1epdf.html#62c5b8ff71d9ebe6cd58d3c342eb1dc8" title="Identified of the random variable.">rv</a> ),<a class="code" href="classbdm_1_1eDirich.html#f25886a49b4667af61245de81c83b5d2" title="sufficient statistics">beta</a> ( D0.<a class="code" href="classbdm_1_1eDirich.html#f25886a49b4667af61245de81c83b5d2" title="sufficient statistics">beta</a> ) {};
-<a name="l00230"></a><a class="code" href="classbdm_1_1eDirich.html#3290613d31d58daa8a45a54b003871fc">00230</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="l00231"></a><a class="code" href="classbdm_1_1eDirich.html#cb343355ec791298bb5a3404cd482fb6">00231</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="l00232"></a><a class="code" href="classbdm_1_1eDirich.html#43c547a2507e233706f92712d8c2aacc">00232</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="l00234"></a><a class="code" href="classbdm_1_1eDirich.html#e09a24938e80c3d94b0ee842d1552318">00234</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>{<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="l00235"></a>00235         <span class="keywordflow">return</span> tmp;};
+<a name="l00114"></a>00114 <span class="keyword">public</span>:
+<a name="l00117"></a>00117         
+<a name="l00118"></a>00118         <a class="code" href="classbdm_1_1enorm.html" title="Gaussian density with positive definite (decomposed) covariance matrix.">enorm</a> ( );
+<a name="l00119"></a>00119         <a class="code" href="classbdm_1_1enorm.html" title="Gaussian density with positive definite (decomposed) covariance matrix.">enorm</a> (<span class="keyword">const</span> vec &amp;<a class="code" href="classbdm_1_1enorm.html#c702a194720853570d08b65482f842c7" title="mean value">mu</a>,<span class="keyword">const</span> sq_T &amp;<a class="code" href="classbdm_1_1enorm.html#2d92dde696b2a7a5b10ddef5d22ba2c2" title="Covariance matrix in decomposed form.">R</a> ){set_parameters(mu,R);}
+<a name="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="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="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_1epdf.html#16adac20ec7fe07e1ea0b27d917788ce" title="dimension of the random variable">dim</a> = <a class="code" href="classbdm_1_1egiw.html#23e4d78bea7e98840f3da30e76a2b57a" title="Dimension of the output.">dimx</a>*(<a class="code" href="classbdm_1_1egiw.html#23e4d78bea7e98840f3da30e76a2b57a" title="Dimension of the output.">dimx</a>+<a class="code" href="classbdm_1_1egiw.html#322414c32d9a21a006a5aab0311f64fd" title="Dimension of the regressor.">nPsi</a>); <span class="comment">// size(R) + size(Theta)</span>
+<a name="l00177"></a>00177                 <a class="code" href="classbdm_1_1egiw.html#322414c32d9a21a006a5aab0311f64fd" title="Dimension of the regressor.">nPsi</a> = <a class="code" href="classbdm_1_1egiw.html#ae56852845c6af176fd9017dbebbbd52" title="Extended information matrix of sufficient statistics.">V</a>.<a class="code" href="group__math.html#g96dfb21865db4f5bd36fa70f9b0b1163" title="access function">rows</a>()-<a class="code" href="classbdm_1_1egiw.html#23e4d78bea7e98840f3da30e76a2b57a" title="Dimension of the output.">dimx</a>;
+<a name="l00178"></a>00178                 
+<a name="l00179"></a>00179                 <a class="code" href="classbdm_1_1egiw.html#ae56852845c6af176fd9017dbebbbd52" title="Extended information matrix of sufficient statistics.">V</a>=V0;
+<a name="l00180"></a>00180                 <span class="keywordflow">if</span> ( nu0&lt;0 ) {
+<a name="l00181"></a>00181                         <a class="code" href="classbdm_1_1egiw.html#447eacf19d4f4083872686f044814dc4" title="Number of data records (degrees of freedom) of sufficient statistics.">nu</a> = 0.1 +nPsi +2*xdim +2; <span class="comment">// +2 assures finite expected value of R</span>
+<a name="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 ) {<a class="code" href="classbdm_1_1egiw.html#ae56852845c6af176fd9017dbebbbd52" title="Extended information matrix of sufficient statistics.">V</a>*=p;<a class="code" href="classbdm_1_1egiw.html#447eacf19d4f4083872686f044814dc4" title="Number of data records (degrees of freedom) of sufficient statistics.">nu</a>*=p;};
+<a name="l00198"></a>00198 
+<a name="l00201"></a>00201         
+<a name="l00202"></a>00202         <a class="code" href="classldmat.html" title="Matrix stored in LD form, (commonly known as UD).">ldmat</a>&amp; _V() {<span class="keywordflow">return</span> <a class="code" href="classbdm_1_1egiw.html#ae56852845c6af176fd9017dbebbbd52" title="Extended information matrix of sufficient statistics.">V</a>;}
+<a name="l00203"></a>00203         <span class="keyword">const</span> <a class="code" href="classldmat.html" title="Matrix stored in LD form, (commonly known as UD).">ldmat</a>&amp; _V()<span class="keyword"> const </span>{<span class="keywordflow">return</span> <a class="code" href="classbdm_1_1egiw.html#ae56852845c6af176fd9017dbebbbd52" title="Extended information matrix of sufficient statistics.">V</a>;}
+<a name="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="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="l00225"></a><a class="code" href="classbdm_1_1eDirich.html#d5137485050ca8d67549b514896f602d">00225</a>         <a class="code" href="classbdm_1_1eDirich.html#d5137485050ca8d67549b514896f602d" title="Default constructor.">eDirich</a> () : <a class="code" href="classbdm_1_1eEF.html" title="General conjugate exponential family posterior density.">eEF</a> ( ) {};
+<a name="l00227"></a><a class="code" href="classbdm_1_1eDirich.html#31cc8bf709552c9e7286ac16b27c8e2c">00227</a>         <a class="code" href="classbdm_1_1eDirich.html#d5137485050ca8d67549b514896f602d" title="Default constructor.">eDirich</a> ( <span class="keyword">const</span> <a class="code" href="classbdm_1_1eDirich.html" title="Dirichlet posterior density.">eDirich</a> &amp;D0 ) : <a class="code" href="classbdm_1_1eEF.html" title="General conjugate exponential family posterior density.">eEF</a> () {<a class="code" href="classbdm_1_1eDirich.html#a06af2376976a33e1eceaed7e8da75a5" title="Set internal parameters.">set_parameters</a> ( D0.<a class="code" href="classbdm_1_1eDirich.html#f25886a49b4667af61245de81c83b5d2" title="sufficient statistics">beta</a> );};
+<a name="l00228"></a><a class="code" href="classbdm_1_1eDirich.html#3290613d31d58daa8a45a54b003871fc">00228</a>         vec <a class="code" href="classbdm_1_1eDirich.html#3290613d31d58daa8a45a54b003871fc" title="Returns a sample,  from density .">sample</a>()<span class="keyword"> const </span>{it_error ( <span class="stringliteral">"Not implemented"</span> );<span class="keywordflow">return</span> vec_1 ( 0.0 );};
+<a name="l00229"></a><a class="code" href="classbdm_1_1eDirich.html#cb343355ec791298bb5a3404cd482fb6">00229</a>         vec <a class="code" href="classbdm_1_1eDirich.html#cb343355ec791298bb5a3404cd482fb6" title="return expected value">mean</a>()<span class="keyword"> const </span>{<span class="keywordflow">return</span> <a class="code" href="classbdm_1_1eDirich.html#f25886a49b4667af61245de81c83b5d2" title="sufficient statistics">beta</a>/<a class="code" href="classbdm_1_1eDirich.html#ee9db192a6f0ab7b29c33b2a18a5e1b4" title="speedup variable">gamma</a>;};
+<a name="l00230"></a><a class="code" href="classbdm_1_1eDirich.html#43c547a2507e233706f92712d8c2aacc">00230</a>         vec <a class="code" href="classbdm_1_1eDirich.html#43c547a2507e233706f92712d8c2aacc" title="return expected variance (not covariance!)">variance</a>()<span class="keyword"> const </span>{<span class="keywordflow">return</span> elem_mult ( <a class="code" href="classbdm_1_1eDirich.html#f25886a49b4667af61245de81c83b5d2" title="sufficient statistics">beta</a>, ( <a class="code" href="classbdm_1_1eDirich.html#f25886a49b4667af61245de81c83b5d2" title="sufficient statistics">beta</a>+1 ) ) / ( <a class="code" href="classbdm_1_1eDirich.html#ee9db192a6f0ab7b29c33b2a18a5e1b4" title="speedup variable">gamma</a>* ( <a class="code" href="classbdm_1_1eDirich.html#ee9db192a6f0ab7b29c33b2a18a5e1b4" title="speedup variable">gamma</a>+1 ) );}
+<a name="l00232"></a><a class="code" href="classbdm_1_1eDirich.html#e09a24938e80c3d94b0ee842d1552318">00232</a>         <span class="keywordtype">double</span> <a class="code" href="classbdm_1_1eDirich.html#e09a24938e80c3d94b0ee842d1552318" title="In this instance, val is ...">evallog_nn</a> ( <span class="keyword">const</span> vec &amp;val )<span class="keyword"> const </span>{
+<a name="l00233"></a>00233                 <span class="keywordtype">double</span> tmp; tmp= ( <a class="code" href="classbdm_1_1eDirich.html#f25886a49b4667af61245de81c83b5d2" title="sufficient statistics">beta</a>-1 ) *log ( val );               it_assert_debug ( std::isfinite ( tmp ),<span class="stringliteral">"Infinite value"</span> );
+<a name="l00234"></a>00234                 <span class="keywordflow">return</span> tmp;
+<a name="l00235"></a>00235         };
 <a name="l00236"></a><a class="code" href="classbdm_1_1eDirich.html#279a99f6266c82fe2273e83841f19eb2">00236</a>         <span class="keywordtype">double</span> <a class="code" href="classbdm_1_1eDirich.html#279a99f6266c82fe2273e83841f19eb2" title="logarithm of the normalizing constant, ">lognc</a> ()<span class="keyword"> const </span>{
 <a name="l00237"></a>00237                 <span class="keywordtype">double</span> tmp;
@@ -169 +168 @@
 <a name="l00240"></a>00240                 <span class="keywordflow">for</span> ( <span class="keywordtype">int</span> i=0;i&lt;<a class="code" href="classbdm_1_1eDirich.html#f25886a49b4667af61245de81c83b5d2" title="sufficient statistics">beta</a>.length();i++ ) {lgb+=lgamma ( <a class="code" href="classbdm_1_1eDirich.html#f25886a49b4667af61245de81c83b5d2" title="sufficient statistics">beta</a> ( i ) );}
 <a name="l00241"></a>00241                 tmp= lgb-lgamma ( gam );
-<a name="l00242"></a>00242                 it_assert_debug(std::isfinite(tmp),<span class="stringliteral">"Infinite value"</span>);
+<a name="l00242"></a>00242                 it_assert_debug ( std::isfinite ( tmp ),<span class="stringliteral">"Infinite value"</span> );
 <a name="l00243"></a>00243                 <span class="keywordflow">return</span> tmp;
 <a name="l00244"></a>00244         };
 <a name="l00246"></a><a class="code" href="classbdm_1_1eDirich.html#175e0add26d2105c28d8121eefb9e324">00246</a>         vec&amp; <a class="code" href="classbdm_1_1eDirich.html#175e0add26d2105c28d8121eefb9e324" title="access function">_beta</a>()  {<span class="keywordflow">return</span> <a class="code" href="classbdm_1_1eDirich.html#f25886a49b4667af61245de81c83b5d2" title="sufficient statistics">beta</a>;}
 <a name="l00248"></a><a class="code" href="classbdm_1_1eDirich.html#a06af2376976a33e1eceaed7e8da75a5">00248</a>         <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1eDirich.html#a06af2376976a33e1eceaed7e8da75a5" title="Set internal parameters.">set_parameters</a> ( <span class="keyword">const</span> vec &amp;beta0 ) {
-<a name="l00249"></a>00249                 <span class="keywordflow">if</span> ( beta0.length() !=<a class="code" href="classbdm_1_1eDirich.html#f25886a49b4667af61245de81c83b5d2" title="sufficient statistics">beta</a>.length() ) {
-<a name="l00250"></a>00250                         it_assert_debug ( <a class="code" href="classbdm_1_1epdf.html#62c5b8ff71d9ebe6cd58d3c342eb1dc8" title="Identified of the random variable.">rv</a>.<a class="code" href="classbdm_1_1RV.html#e9ec8c3e756651ff352ab5e3d3acda4b" title="Return length (number of entries) of the RV.">length</a>() ==1,<span class="stringliteral">"Undefined"</span> );
-<a name="l00251"></a>00251                         <a class="code" href="classbdm_1_1epdf.html#62c5b8ff71d9ebe6cd58d3c342eb1dc8" title="Identified of the random variable.">rv</a>.<a class="code" href="classbdm_1_1RV.html#0b2c9e73ff66847c3644ebc3eb559a03" title="access function">set_size</a> ( 0,beta0.length() );
-<a name="l00252"></a>00252                 }
-<a name="l00253"></a>00253                 <a class="code" href="classbdm_1_1eDirich.html#f25886a49b4667af61245de81c83b5d2" title="sufficient statistics">beta</a>= beta0;
-<a name="l00254"></a>00254                 <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="l00255"></a>00255         }
-<a name="l00256"></a>00256 };
-<a name="l00257"></a>00257 
-<a name="l00259"></a><a class="code" href="classbdm_1_1multiBM.html">00259</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="l00260"></a>00260 <span class="keyword">protected</span>:
-<a name="l00262"></a><a class="code" href="classbdm_1_1multiBM.html#9ecc6878abbd20eb8d8e43b6ab3f941a">00262</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="l00264"></a><a class="code" href="classbdm_1_1multiBM.html#044263356944c92209eecd39a5187d25">00264</a>         vec &amp;<a class="code" href="classbdm_1_1multiBM.html#044263356944c92209eecd39a5187d25" title="Pointer inside est to sufficient statistics.">beta</a>;
-<a name="l00265"></a>00265 <span class="keyword">public</span>:
-<a name="l00267"></a><a class="code" href="classbdm_1_1multiBM.html#65dc7567b67ce86a8f339dd496ed8e88">00267</a>         <a class="code" href="classbdm_1_1multiBM.html#65dc7567b67ce86a8f339dd496ed8e88" title="Default constructor.">multiBM</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_1BM.html#18d6db4af8ee42077741d9e3618153ca" title="Random variable of the posterior.">rv</a>, <span class="keyword">const</span> vec beta0 ) : <a class="code" href="classbdm_1_1BMEF.html" title="Estimator for Exponential family.">BMEF</a> ( rv ),<a class="code" href="classbdm_1_1multiBM.html#9ecc6878abbd20eb8d8e43b6ab3f941a" title="Conjugate prior and posterior.">est</a> ( rv,beta0 ),<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="l00269"></a><a class="code" href="classbdm_1_1multiBM.html#c4378cf8037f6bed29c74eea63344b31">00269</a>         <a class="code" href="classbdm_1_1multiBM.html#65dc7567b67ce86a8f339dd496ed8e88" title="Default constructor.">multiBM</a> ( <span class="keyword">const</span> <a class="code" href="classbdm_1_1multiBM.html" title="Estimator for Multinomial density.">multiBM</a> &amp;B ) : <a class="code" href="classbdm_1_1BMEF.html" title="Estimator for Exponential family.">BMEF</a> ( B ),<a class="code" href="classbdm_1_1multiBM.html#9ecc6878abbd20eb8d8e43b6ab3f941a" title="Conjugate prior and posterior.">est</a> ( <a class="code" href="classbdm_1_1BM.html#18d6db4af8ee42077741d9e3618153ca" title="Random variable of the posterior.">rv</a>,B.<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#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="l00271"></a><a class="code" href="classbdm_1_1multiBM.html#dbe6b90d410dc062a233d1dc09eeba52">00271</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="l00272"></a><a class="code" href="classbdm_1_1multiBM.html#1e4bf41b61937fd80f34049742e23f95">00272</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="l00273"></a>00273                 <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="l00274"></a>00274                 <a class="code" href="classbdm_1_1multiBM.html#044263356944c92209eecd39a5187d25" title="Pointer inside est to sufficient statistics.">beta</a>+=dt;
-<a name="l00275"></a>00275                 <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="l00276"></a>00276         }
-<a name="l00277"></a><a class="code" href="classbdm_1_1multiBM.html#e157b607c1e3fa91d42aeea44458e2bf">00277</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="l00278"></a>00278                 <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="l00279"></a>00279                 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="l00280"></a>00280 
-<a name="l00281"></a>00281                 <span class="keywordtype">double</span> lll;
-<a name="l00282"></a>00282                 <span class="keywordflow">if</span> ( <a class="code" href="classbdm_1_1BMEF.html#1331865e10fb1ccef65bb4c47fa3be64" title="forgetting factor">frg</a>&lt;1.0 )
-<a name="l00283"></a>00283                         {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="l00284"></a>00284                 <span class="keywordflow">else</span>
-<a name="l00285"></a>00285                         <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="l00286"></a>00286                         <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="l00287"></a>00287 
-<a name="l00288"></a>00288                 beta+=dt;
-<a name="l00289"></a>00289                 <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="l00290"></a>00290         }
-<a name="l00291"></a><a class="code" href="classbdm_1_1multiBM.html#aaeb18c989088feb8d26d300e4971732">00291</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="l00292"></a>00292                 <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="l00293"></a>00293                 <span class="comment">// sum(beta) should be equal to sum(B.beta)</span>
-<a name="l00294"></a>00294                 <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="l00295"></a>00295                 <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="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_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="l00297"></a>00297         }
-<a name="l00298"></a><a class="code" href="classbdm_1_1multiBM.html#98c22316ecfef589989baca261713c8d">00298</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>&amp; <a class="code" href="classbdm_1_1multiBM.html#98c22316ecfef589989baca261713c8d" title="Returns a reference to the epdf representing posterior density on parameters.">_epdf</a>()<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="l00299"></a><a class="code" href="classbdm_1_1multiBM.html#c996f6b9ca930182030e1027318f1ca6">00299</a>         <span class="keyword">const</span> <a class="code" href="classbdm_1_1eDirich.html" title="Dirichlet posterior density.">eDirich</a>* <a class="code" href="classbdm_1_1multiBM.html#c996f6b9ca930182030e1027318f1ca6" title="Returns a pointer to the epdf representing posterior density on parameters. Use with...">_e</a>()<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="l00300"></a>00300         <span class="keywordtype">void</span> set_parameters ( <span class="keyword">const</span> vec &amp;beta0 ) {
-<a name="l00301"></a>00301                 <a class="code" href="classbdm_1_1multiBM.html#9ecc6878abbd20eb8d8e43b6ab3f941a" title="Conjugate prior and posterior.">est</a>.<a class="code" href="classbdm_1_1eDirich.html#a06af2376976a33e1eceaed7e8da75a5" title="Set internal parameters.">set_parameters</a> ( beta0 );
-<a name="l00302"></a>00302                 <a class="code" href="classbdm_1_1BM.html#18d6db4af8ee42077741d9e3618153ca" title="Random variable of the posterior.">rv</a> = <a class="code" href="classbdm_1_1multiBM.html#9ecc6878abbd20eb8d8e43b6ab3f941a" title="Conjugate prior and posterior.">est</a>.<a class="code" href="classbdm_1_1epdf.html#a4ab378d5e004c3ff3e2d4e64f7bba21" title="access function, possibly dangerous!">_rv</a>();
-<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>.<a class="code" href="classbdm_1_1eDirich.html#279a99f6266c82fe2273e83841f19eb2" title="logarithm of the normalizing constant, ">lognc</a>();}
-<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="l00324"></a><a class="code" href="classbdm_1_1egamma.html#4dafabaa0881300b18f791bc614ef487">00324</a>         <a class="code" href="classbdm_1_1egamma.html#4dafabaa0881300b18f791bc614ef487" title="Default constructor.">egamma</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="Identified of the random variable.">rv</a> ) :<a class="code" href="classbdm_1_1eEF.html" title="General conjugate exponential family posterior density.">eEF</a> ( rv ), <a class="code" href="classbdm_1_1egamma.html#0901ec983e66b8337aaa506e13b122fa" title="Vector .">alpha</a>(rv.count()), <a class="code" href="classbdm_1_1egamma.html#457bfb1ccb2057df85073e519a15ccc1" title="Vector .">beta</a>(rv.count()) {};
-<a name="l00326"></a><a class="code" href="classbdm_1_1egamma.html#749f82293ff23a8319c1bf52489d2ed2">00326</a>         <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1egamma.html#749f82293ff23a8319c1bf52489d2ed2" title="Sets parameters.">set_parameters</a> ( <span class="keyword">const</span> vec &amp;a, <span class="keyword">const</span> vec &amp;b ) {<a class="code" href="classbdm_1_1egamma.html#0901ec983e66b8337aaa506e13b122fa" title="Vector .">alpha</a>=a,<a class="code" href="classbdm_1_1egamma.html#457bfb1ccb2057df85073e519a15ccc1" title="Vector .">beta</a>=b;};
-<a name="l00327"></a>00327         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="l00329"></a>00329 <span class="comment">//      mat sample ( int N ) const;</span>
-<a name="l00330"></a>00330         <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="l00331"></a>00331         <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="l00333"></a><a class="code" href="classbdm_1_1egamma.html#498c1fe5e8382ab2f97d629849741855">00333</a>         <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1egamma.html#498c1fe5e8382ab2f97d629849741855" title="Returns poiter to alpha and beta. Potentially dengerous: use with care!">_param</a> ( vec* &amp;a, vec* &amp;b ) {a=&amp;<a class="code" href="classbdm_1_1egamma.html#0901ec983e66b8337aaa506e13b122fa" title="Vector .">alpha</a>;b=&amp;<a class="code" href="classbdm_1_1egamma.html#457bfb1ccb2057df85073e519a15ccc1" title="Vector .">beta</a>;};
-<a name="l00334"></a><a class="code" href="classbdm_1_1egamma.html#49d256c42cce14c6faa56ec242b57e85">00334</a>         vec <a class="code" href="classbdm_1_1egamma.html#49d256c42cce14c6faa56ec242b57e85" title="return expected value">mean</a>()<span class="keyword"> const </span>{<span class="keywordflow">return</span> elem_div(<a class="code" href="classbdm_1_1egamma.html#0901ec983e66b8337aaa506e13b122fa" title="Vector .">alpha</a>,<a class="code" href="classbdm_1_1egamma.html#457bfb1ccb2057df85073e519a15ccc1" title="Vector .">beta</a>);}
-<a name="l00335"></a><a class="code" href="classbdm_1_1egamma.html#36986cc01917cd0796fadc17125bdec1">00335</a>         vec <a class="code" href="classbdm_1_1egamma.html#36986cc01917cd0796fadc17125bdec1" title="return expected variance (not covariance!)">variance</a>()<span class="keyword"> const </span>{<span class="keywordflow">return</span> elem_div(<a class="code" href="classbdm_1_1egamma.html#0901ec983e66b8337aaa506e13b122fa" title="Vector .">alpha</a>,elem_mult(<a class="code" href="classbdm_1_1egamma.html#457bfb1ccb2057df85073e519a15ccc1" title="Vector .">beta</a>,<a class="code" href="classbdm_1_1egamma.html#457bfb1ccb2057df85073e519a15ccc1" title="Vector .">beta</a>)); }
-<a name="l00336"></a>00336 };
-<a name="l00337"></a>00337 
-<a name="l00352"></a><a class="code" href="classbdm_1_1eigamma.html">00352</a> <span class="keyword">class </span><a class="code" href="classbdm_1_1eigamma.html" title="Inverse-Gamma posterior density.">eigamma</a> : <span class="keyword">public</span> <a class="code" href="classbdm_1_1eEF.html" title="General conjugate exponential family posterior density.">eEF</a> {
-<a name="l00353"></a>00353         <span class="keyword">protected</span>:
-<a name="l00355"></a><a class="code" href="classbdm_1_1eigamma.html#e2c49c77e04a96a9e6a4a628318ceb73">00355</a>                 vec* <a class="code" href="classbdm_1_1eigamma.html#e2c49c77e04a96a9e6a4a628318ceb73" title="Vector .">alpha</a>;
-<a name="l00357"></a><a class="code" href="classbdm_1_1eigamma.html#b2c62f2e869d1304a4055f6a7ac59cde">00357</a>                 vec* <a class="code" href="classbdm_1_1eigamma.html#b2c62f2e869d1304a4055f6a7ac59cde" title="Vector  (in fact it is 1/beta as used in definition of iG).">beta</a>;
-<a name="l00359"></a><a class="code" href="classbdm_1_1eigamma.html#3e5c03201f073033a7db894fa15ddb96">00359</a>                 <a class="code" href="classbdm_1_1egamma.html" title="Gamma posterior density.">egamma</a> <a class="code" href="classbdm_1_1eigamma.html#3e5c03201f073033a7db894fa15ddb96" title="internal egamma">eg</a>;
-<a name="l00360"></a>00360         <span class="keyword">public</span> :
-<a name="l00362"></a><a class="code" href="classbdm_1_1eigamma.html#34a8d2cd08399c3449e2efcda6ea2f89">00362</a>                 <a class="code" href="classbdm_1_1eigamma.html#34a8d2cd08399c3449e2efcda6ea2f89" title="Default constructor.">eigamma</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="Identified of the random variable.">rv</a> ) :<a class="code" href="classbdm_1_1eEF.html" title="General conjugate exponential family posterior density.">eEF</a> ( rv ), <a class="code" href="classbdm_1_1eigamma.html#3e5c03201f073033a7db894fa15ddb96" title="internal egamma">eg</a>(rv) {<a class="code" href="classbdm_1_1eigamma.html#3e5c03201f073033a7db894fa15ddb96" title="internal egamma">eg</a>.<a class="code" href="classbdm_1_1egamma.html#498c1fe5e8382ab2f97d629849741855" title="Returns poiter to alpha and beta. Potentially dengerous: use with care!">_param</a>(<a class="code" href="classbdm_1_1eigamma.html#e2c49c77e04a96a9e6a4a628318ceb73" title="Vector .">alpha</a>,<a class="code" href="classbdm_1_1eigamma.html#b2c62f2e869d1304a4055f6a7ac59cde" title="Vector  (in fact it is 1/beta as used in definition of iG).">beta</a>);};
-<a name="l00364"></a><a class="code" href="classbdm_1_1eigamma.html#09e616c95f31acddf7dfef96d1c5d645">00364</a>                 <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1eigamma.html#09e616c95f31acddf7dfef96d1c5d645" title="Sets parameters.">set_parameters</a> ( <span class="keyword">const</span> vec &amp;a, <span class="keyword">const</span> vec &amp;b ) {*<a class="code" href="classbdm_1_1eigamma.html#e2c49c77e04a96a9e6a4a628318ceb73" title="Vector .">alpha</a>=a,*<a class="code" href="classbdm_1_1eigamma.html#b2c62f2e869d1304a4055f6a7ac59cde" title="Vector  (in fact it is 1/beta as used in definition of iG).">beta</a>=b;};
-<a name="l00365"></a><a class="code" href="classbdm_1_1eigamma.html#3aff7bf25ddac27731c60826fcfd878f">00365</a>                 vec <a class="code" href="classbdm_1_1eigamma.html#3aff7bf25ddac27731c60826fcfd878f" title="Returns a sample,  from density .">sample</a>()<span class="keyword"> const </span>{<span class="keywordflow">return</span> 1.0/<a class="code" href="classbdm_1_1eigamma.html#3e5c03201f073033a7db894fa15ddb96" title="internal egamma">eg</a>.<a class="code" href="classbdm_1_1egamma.html#6ed82f0fd05f6002487256d8e75a0bbd" title="Returns a sample,  from density .">sample</a>();};
-<a name="l00367"></a>00367 <span class="comment">//      mat sample ( int N ) const;</span>
-<a name="l00368"></a><a class="code" href="classbdm_1_1eigamma.html#9e26c80c8e6708bfcf2e684958af6f91">00368</a>                 <span class="keywordtype">double</span> <a class="code" href="classbdm_1_1eigamma.html#9e26c80c8e6708bfcf2e684958af6f91" title="TODO: is it used anywhere?">evallog</a> ( <span class="keyword">const</span> vec &amp;val )<span class="keyword"> const </span>{<span class="keywordflow">return</span> <a class="code" href="classbdm_1_1eigamma.html#3e5c03201f073033a7db894fa15ddb96" title="internal egamma">eg</a>.<a class="code" href="classbdm_1_1egamma.html#a8e11e5a580ff42a1b205974c60768c6" title="TODO: is it used anywhere?">evallog</a>(val);};
-<a name="l00369"></a><a class="code" href="classbdm_1_1eigamma.html#a52ac6d523e2fe05642d1f50fe66aec2">00369</a>                 <span class="keywordtype">double</span> <a class="code" href="classbdm_1_1eigamma.html#a52ac6d523e2fe05642d1f50fe66aec2" title="logarithm of the normalizing constant, ">lognc</a> ()<span class="keyword"> const </span>{<span class="keywordflow">return</span> <a class="code" href="classbdm_1_1eigamma.html#3e5c03201f073033a7db894fa15ddb96" title="internal egamma">eg</a>.<a class="code" href="classbdm_1_1egamma.html#9a66cbd100e8520c769ccb3c451f86f8" title="logarithm of the normalizing constant, ">lognc</a>();};
-<a name="l00371"></a><a class="code" href="classbdm_1_1eigamma.html#57b9ee79ef5d2cea243bbe6b274a2abe">00371</a>                 <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1eigamma.html#57b9ee79ef5d2cea243bbe6b274a2abe" title="Returns poiter to alpha and beta. Potentially dangerous: use with care!">_param</a> ( vec* &amp;a, vec* &amp;b ) {a=<a class="code" href="classbdm_1_1eigamma.html#e2c49c77e04a96a9e6a4a628318ceb73" title="Vector .">alpha</a>;b=<a class="code" href="classbdm_1_1eigamma.html#b2c62f2e869d1304a4055f6a7ac59cde" title="Vector  (in fact it is 1/beta as used in definition of iG).">beta</a>;};
-<a name="l00372"></a><a class="code" href="classbdm_1_1eigamma.html#46cecb295edbabd28120cb0f6f572bcb">00372</a>                 vec <a class="code" href="classbdm_1_1eigamma.html#46cecb295edbabd28120cb0f6f572bcb" title="return expected value">mean</a>()<span class="keyword"> const </span>{<span class="keywordflow">return</span> elem_div(*<a class="code" href="classbdm_1_1eigamma.html#b2c62f2e869d1304a4055f6a7ac59cde" title="Vector  (in fact it is 1/beta as used in definition of iG).">beta</a>,*<a class="code" href="classbdm_1_1eigamma.html#e2c49c77e04a96a9e6a4a628318ceb73" title="Vector .">alpha</a>-1);}
-<a name="l00373"></a><a class="code" href="classbdm_1_1eigamma.html#c2c696f8c668e9f65392c9449f6a5133">00373</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="return expected value">mean</a>(); <span class="keywordflow">return</span> elem_div(elem_mult(mea,mea),*<a class="code" href="classbdm_1_1eigamma.html#e2c49c77e04a96a9e6a4a628318ceb73" title="Vector .">alpha</a>-2);}
-<a name="l00374"></a>00374 };
-<a name="l00375"></a>00375 <span class="comment">/*</span>
-<a name="l00377"></a>00377 <span class="comment">class emix : public epdf {</span>
-<a name="l00378"></a>00378 <span class="comment">protected:</span>
-<a name="l00379"></a>00379 <span class="comment">        int n;</span>
-<a name="l00380"></a>00380 <span class="comment">        vec &amp;w;</span>
-<a name="l00381"></a>00381 <span class="comment">        Array&lt;epdf*&gt; Coms;</span>
-<a name="l00382"></a>00382 <span class="comment">public:</span>
-<a name="l00384"></a>00384 <span class="comment">        emix ( const RV &amp;rv, vec &amp;w0): epdf(rv), n(w0.length()), w(w0), Coms(n) {};</span>
-<a name="l00385"></a>00385 <span class="comment">        void set_parameters( int &amp;i, double wi, epdf* ep){w(i)=wi;Coms(i)=ep;}</span>
-<a name="l00386"></a>00386 <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="l00387"></a>00387 <span class="comment">        vec sample() {it_error ( "Not implemented" );return 0;}</span>
-<a name="l00388"></a>00388 <span class="comment">};</span>
-<a name="l00389"></a>00389 <span class="comment">*/</span>
+<a name="l00249"></a>00249                 <a class="code" href="classbdm_1_1eDirich.html#f25886a49b4667af61245de81c83b5d2" title="sufficient statistics">beta</a>= beta0;
+<a name="l00250"></a>00250                 <a class="code" href="classbdm_1_1epdf.html#16adac20ec7fe07e1ea0b27d917788ce" title="dimension of the random variable">dim</a> = <a class="code" href="classbdm_1_1eDirich.html#f25886a49b4667af61245de81c83b5d2" title="sufficient statistics">beta</a>.length();
+<a name="l00251"></a>00251                 <a class="code" href="classbdm_1_1eDirich.html#ee9db192a6f0ab7b29c33b2a18a5e1b4" title="speedup variable">gamma</a> = sum ( <a class="code" href="classbdm_1_1eDirich.html#f25886a49b4667af61245de81c83b5d2" title="sufficient statistics">beta</a> );
+<a name="l00252"></a>00252         }
+<a name="l00253"></a>00253 };
+<a name="l00254"></a>00254 
+<a name="l00256"></a><a class="code" href="classbdm_1_1multiBM.html">00256</a> <span class="keyword">class </span><a class="code" href="classbdm_1_1multiBM.html" title="Estimator for Multinomial density.">multiBM</a> : <span class="keyword">public</span> <a class="code" href="classbdm_1_1BMEF.html" title="Estimator for Exponential family.">BMEF</a> {
+<a name="l00257"></a>00257 <span class="keyword">protected</span>:
+<a name="l00259"></a><a class="code" href="classbdm_1_1multiBM.html#9ecc6878abbd20eb8d8e43b6ab3f941a">00259</a>         <a class="code" href="classbdm_1_1eDirich.html" title="Dirichlet posterior density.">eDirich</a> <a class="code" href="classbdm_1_1multiBM.html#9ecc6878abbd20eb8d8e43b6ab3f941a" title="Conjugate prior and posterior.">est</a>;
+<a name="l00261"></a><a class="code" href="classbdm_1_1multiBM.html#044263356944c92209eecd39a5187d25">00261</a>         vec &amp;<a class="code" href="classbdm_1_1multiBM.html#044263356944c92209eecd39a5187d25" title="Pointer inside est to sufficient statistics.">beta</a>;
+<a name="l00262"></a>00262 <span class="keyword">public</span>:
+<a name="l00264"></a><a class="code" href="classbdm_1_1multiBM.html#c4dd6d9522a8a605776d21bac9bd9daf">00264</a>         <a class="code" href="classbdm_1_1multiBM.html#c4dd6d9522a8a605776d21bac9bd9daf" title="Default constructor.">multiBM</a> ( ) : <a class="code" href="classbdm_1_1BMEF.html" title="Estimator for Exponential family.">BMEF</a> ( ),<a class="code" href="classbdm_1_1multiBM.html#9ecc6878abbd20eb8d8e43b6ab3f941a" title="Conjugate prior and posterior.">est</a> ( ),<a class="code" href="classbdm_1_1multiBM.html#044263356944c92209eecd39a5187d25" title="Pointer inside est to sufficient statistics.">beta</a> ( <a class="code" href="classbdm_1_1multiBM.html#9ecc6878abbd20eb8d8e43b6ab3f941a" title="Conjugate prior and posterior.">est</a>._beta() ) {<span class="keywordflow">if</span> ( <a class="code" href="classbdm_1_1multiBM.html#044263356944c92209eecd39a5187d25" title="Pointer inside est to sufficient statistics.">beta</a>.length() &gt;0 ) {<a class="code" href="classbdm_1_1BMEF.html#06e7b3ac03e10017d4288c76888e2865" title="cached value of lognc() in the previous step (used in evaluation of ll )">last_lognc</a>=<a class="code" href="classbdm_1_1multiBM.html#9ecc6878abbd20eb8d8e43b6ab3f941a" title="Conjugate prior and posterior.">est</a>.<a class="code" href="classbdm_1_1eDirich.html#279a99f6266c82fe2273e83841f19eb2" title="logarithm of the normalizing constant, ">lognc</a>();}<span class="keywordflow">else</span>{<a class="code" href="classbdm_1_1BMEF.html#06e7b3ac03e10017d4288c76888e2865" title="cached value of lognc() in the previous step (used in evaluation of ll )">last_lognc</a>=0.0;}}
+<a name="l00266"></a><a class="code" href="classbdm_1_1multiBM.html#c4378cf8037f6bed29c74eea63344b31">00266</a>         <a class="code" href="classbdm_1_1multiBM.html#c4dd6d9522a8a605776d21bac9bd9daf" title="Default constructor.">multiBM</a> ( <span class="keyword">const</span> <a class="code" href="classbdm_1_1multiBM.html" title="Estimator for Multinomial density.">multiBM</a> &amp;B ) : <a class="code" href="classbdm_1_1BMEF.html" title="Estimator for Exponential family.">BMEF</a> ( B ),<a class="code" href="classbdm_1_1multiBM.html#9ecc6878abbd20eb8d8e43b6ab3f941a" title="Conjugate prior and posterior.">est</a> ( B.<a class="code" href="classbdm_1_1multiBM.html#9ecc6878abbd20eb8d8e43b6ab3f941a" title="Conjugate prior and posterior.">est</a> ),<a class="code" href="classbdm_1_1multiBM.html#044263356944c92209eecd39a5187d25" title="Pointer inside est to sufficient statistics.">beta</a> ( <a class="code" href="classbdm_1_1multiBM.html#9ecc6878abbd20eb8d8e43b6ab3f941a" title="Conjugate prior and posterior.">est</a>._beta() ) {}
+<a name="l00268"></a><a class="code" href="classbdm_1_1multiBM.html#dbe6b90d410dc062a233d1dc09eeba52">00268</a>         <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1multiBM.html#dbe6b90d410dc062a233d1dc09eeba52" title="Sets sufficient statistics to match that of givefrom mB0.">set_statistics</a> ( <span class="keyword">const</span> <a class="code" href="classbdm_1_1BM.html" title="Bayesian Model of a system, i.e. all uncertainty is modeled by probabilities.">BM</a>* mB0 ) {<span class="keyword">const</span> <a class="code" href="classbdm_1_1multiBM.html" title="Estimator for Multinomial density.">multiBM</a>* mB=<span class="keyword">dynamic_cast&lt;</span><span class="keyword">const </span><a class="code" href="classbdm_1_1multiBM.html" title="Estimator for Multinomial density.">multiBM</a>*<span class="keyword">&gt;</span> ( mB0 ); <a class="code" href="classbdm_1_1multiBM.html#044263356944c92209eecd39a5187d25" title="Pointer inside est to sufficient statistics.">beta</a>=mB-&gt;<a class="code" href="classbdm_1_1multiBM.html#044263356944c92209eecd39a5187d25" title="Pointer inside est to sufficient statistics.">beta</a>;}
+<a name="l00269"></a><a class="code" href="classbdm_1_1multiBM.html#1e4bf41b61937fd80f34049742e23f95">00269</a>         <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1multiBM.html#1e4bf41b61937fd80f34049742e23f95" title="Incremental Bayes rule.">bayes</a> ( <span class="keyword">const</span> vec &amp;dt ) {
+<a name="l00270"></a>00270                 <span class="keywordflow">if</span> ( <a class="code" href="classbdm_1_1BMEF.html#1331865e10fb1ccef65bb4c47fa3be64" title="forgetting factor">frg</a>&lt;1.0 ) {<a class="code" href="classbdm_1_1multiBM.html#044263356944c92209eecd39a5187d25" title="Pointer inside est to sufficient statistics.">beta</a>*=<a class="code" href="classbdm_1_1BMEF.html#1331865e10fb1ccef65bb4c47fa3be64" title="forgetting factor">frg</a>;<a class="code" href="classbdm_1_1BMEF.html#06e7b3ac03e10017d4288c76888e2865" title="cached value of lognc() in the previous step (used in evaluation of ll )">last_lognc</a>=<a class="code" href="classbdm_1_1multiBM.html#9ecc6878abbd20eb8d8e43b6ab3f941a" title="Conjugate prior and posterior.">est</a>.<a class="code" href="classbdm_1_1eDirich.html#279a99f6266c82fe2273e83841f19eb2" title="logarithm of the normalizing constant, ">lognc</a>();}
+<a name="l00271"></a>00271                 <a class="code" href="classbdm_1_1multiBM.html#044263356944c92209eecd39a5187d25" title="Pointer inside est to sufficient statistics.">beta</a>+=dt;
+<a name="l00272"></a>00272                 <span class="keywordflow">if</span> ( <a class="code" href="classbdm_1_1BM.html#faff0ad12556fe7dc0e2807d4fd938ee" title="If true, the filter will compute likelihood of the data record and store it in ll...">evalll</a> ) {<a class="code" href="classbdm_1_1BM.html#4064b6559d962633e4372b12f4cd204a" title="Logarithm of marginalized data likelihood.">ll</a>=<a class="code" href="classbdm_1_1multiBM.html#9ecc6878abbd20eb8d8e43b6ab3f941a" title="Conjugate prior and posterior.">est</a>.<a class="code" href="classbdm_1_1eDirich.html#279a99f6266c82fe2273e83841f19eb2" title="logarithm of the normalizing constant, ">lognc</a>()-<a class="code" href="classbdm_1_1BMEF.html#06e7b3ac03e10017d4288c76888e2865" title="cached value of lognc() in the previous step (used in evaluation of ll )">last_lognc</a>;}
+<a name="l00273"></a>00273         }
+<a name="l00274"></a><a class="code" href="classbdm_1_1multiBM.html#e157b607c1e3fa91d42aeea44458e2bf">00274</a>         <span class="keywordtype">double</span> <a class="code" href="classbdm_1_1multiBM.html#e157b607c1e3fa91d42aeea44458e2bf">logpred</a> ( <span class="keyword">const</span> vec &amp;dt )<span class="keyword"> const </span>{
+<a name="l00275"></a>00275                 <a class="code" href="classbdm_1_1eDirich.html" title="Dirichlet posterior density.">eDirich</a> pred ( <a class="code" href="classbdm_1_1multiBM.html#9ecc6878abbd20eb8d8e43b6ab3f941a" title="Conjugate prior and posterior.">est</a> );
+<a name="l00276"></a>00276                 vec &amp;<a class="code" href="classbdm_1_1multiBM.html#044263356944c92209eecd39a5187d25" title="Pointer inside est to sufficient statistics.">beta</a> = pred.<a class="code" href="classbdm_1_1eDirich.html#175e0add26d2105c28d8121eefb9e324" title="access function">_beta</a>();
+<a name="l00277"></a>00277 
+<a name="l00278"></a>00278                 <span class="keywordtype">double</span> lll;
+<a name="l00279"></a>00279                 <span class="keywordflow">if</span> ( <a class="code" href="classbdm_1_1BMEF.html#1331865e10fb1ccef65bb4c47fa3be64" title="forgetting factor">frg</a>&lt;1.0 )
+<a name="l00280"></a>00280                         {beta*=<a class="code" href="classbdm_1_1BMEF.html#1331865e10fb1ccef65bb4c47fa3be64" title="forgetting factor">frg</a>;lll=pred.<a class="code" href="classbdm_1_1eDirich.html#279a99f6266c82fe2273e83841f19eb2" title="logarithm of the normalizing constant, ">lognc</a>();}
+<a name="l00281"></a>00281                 <span class="keywordflow">else</span>
+<a name="l00282"></a>00282                         <span class="keywordflow">if</span> ( <a class="code" href="classbdm_1_1BM.html#faff0ad12556fe7dc0e2807d4fd938ee" title="If true, the filter will compute likelihood of the data record and store it in ll...">evalll</a> ) {lll=<a class="code" href="classbdm_1_1BMEF.html#06e7b3ac03e10017d4288c76888e2865" title="cached value of lognc() in the previous step (used in evaluation of ll )">last_lognc</a>;}
+<a name="l00283"></a>00283                         <span class="keywordflow">else</span>{lll=pred.<a class="code" href="classbdm_1_1eDirich.html#279a99f6266c82fe2273e83841f19eb2" title="logarithm of the normalizing constant, ">lognc</a>();}
+<a name="l00284"></a>00284 
+<a name="l00285"></a>00285                 beta+=dt;
+<a name="l00286"></a>00286                 <span class="keywordflow">return</span> pred.<a class="code" href="classbdm_1_1eDirich.html#279a99f6266c82fe2273e83841f19eb2" title="logarithm of the normalizing constant, ">lognc</a>()-lll;
+<a name="l00287"></a>00287         }
+<a name="l00288"></a><a class="code" href="classbdm_1_1multiBM.html#aaeb18c989088feb8d26d300e4971732">00288</a>         <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1multiBM.html#aaeb18c989088feb8d26d300e4971732" title="Flatten the posterior according to the given BMEF (of the same type!).">flatten</a> ( <span class="keyword">const</span> <a class="code" href="classbdm_1_1BMEF.html" title="Estimator for Exponential family.">BMEF</a>* B ) {
+<a name="l00289"></a>00289                 <span class="keyword">const</span> <a class="code" href="classbdm_1_1multiBM.html" title="Estimator for Multinomial density.">multiBM</a>* E=<span class="keyword">dynamic_cast&lt;</span><span class="keyword">const </span><a class="code" href="classbdm_1_1multiBM.html" title="Estimator for Multinomial density.">multiBM</a>*<span class="keyword">&gt;</span> ( B );
+<a name="l00290"></a>00290                 <span class="comment">// sum(beta) should be equal to sum(B.beta)</span>
+<a name="l00291"></a>00291                 <span class="keyword">const</span> vec &amp;Eb=E-&gt;<a class="code" href="classbdm_1_1multiBM.html#044263356944c92209eecd39a5187d25" title="Pointer inside est to sufficient statistics.">beta</a>;<span class="comment">//const_cast&lt;multiBM*&gt; ( E )-&gt;_beta();</span>
+<a name="l00292"></a>00292                 <a class="code" href="classbdm_1_1multiBM.html#044263356944c92209eecd39a5187d25" title="Pointer inside est to sufficient statistics.">beta</a>*= ( sum ( Eb ) /sum ( <a class="code" href="classbdm_1_1multiBM.html#044263356944c92209eecd39a5187d25" title="Pointer inside est to sufficient statistics.">beta</a> ) );
+<a name="l00293"></a>00293                 <span class="keywordflow">if</span> ( <a class="code" href="classbdm_1_1BM.html#faff0ad12556fe7dc0e2807d4fd938ee" title="If true, the filter will compute likelihood of the data record and store it in ll...">evalll</a> ) {<a class="code" href="classbdm_1_1BMEF.html#06e7b3ac03e10017d4288c76888e2865" title="cached value of lognc() in the previous step (used in evaluation of ll )">last_lognc</a>=<a class="code" href="classbdm_1_1multiBM.html#9ecc6878abbd20eb8d8e43b6ab3f941a" title="Conjugate prior and posterior.">est</a>.<a class="code" href="classbdm_1_1eDirich.html#279a99f6266c82fe2273e83841f19eb2" title="logarithm of the normalizing constant, ">lognc</a>();}
+<a name="l00294"></a>00294         }
+<a name="l00295"></a>00295         <span class="keyword">const</span> <a class="code" href="classbdm_1_1epdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a>&amp; _epdf()<span class="keyword"> const </span>{<span class="keywordflow">return</span> <a class="code" href="classbdm_1_1multiBM.html#9ecc6878abbd20eb8d8e43b6ab3f941a" title="Conjugate prior and posterior.">est</a>;};
+<a name="l00296"></a>00296         <span class="keyword">const</span> eDirich* _e()<span class="keyword"> const </span>{<span class="keywordflow">return</span> &amp;<a class="code" href="classbdm_1_1multiBM.html#9ecc6878abbd20eb8d8e43b6ab3f941a" title="Conjugate prior and posterior.">est</a>;};
+<a name="l00297"></a>00297         <span class="keywordtype">void</span> set_parameters ( <span class="keyword">const</span> vec &amp;beta0 ) {
+<a name="l00298"></a>00298                 <a class="code" href="classbdm_1_1multiBM.html#9ecc6878abbd20eb8d8e43b6ab3f941a" title="Conjugate prior and posterior.">est</a>.set_parameters ( beta0 );
+<a name="l00299"></a>00299                 <span class="keywordflow">if</span> ( <a class="code" href="classbdm_1_1BM.html#faff0ad12556fe7dc0e2807d4fd938ee" title="If true, the filter will compute likelihood of the data record and store it in ll...">evalll</a> ) {<a class="code" href="classbdm_1_1BMEF.html#06e7b3ac03e10017d4288c76888e2865" title="cached value of lognc() in the previous step (used in evaluation of ll )">last_lognc</a>=<a class="code" href="classbdm_1_1multiBM.html#9ecc6878abbd20eb8d8e43b6ab3f941a" title="Conjugate prior and posterior.">est</a>.lognc();}
+<a name="l00300"></a>00300         }
+<a name="l00301"></a>00301 };
+<a name="l00302"></a>00302 
+<a name="l00312"></a><a class="code" href="classbdm_1_1egamma.html">00312</a> <span class="keyword">class </span><a class="code" href="classbdm_1_1egamma.html" title="Gamma posterior density.">egamma</a> : <span class="keyword">public</span> <a class="code" href="classbdm_1_1eEF.html" title="General conjugate exponential family posterior density.">eEF</a> {
+<a name="l00313"></a>00313 <span class="keyword">protected</span>:
+<a name="l00315"></a><a class="code" href="classbdm_1_1egamma.html#0901ec983e66b8337aaa506e13b122fa">00315</a>         vec <a class="code" href="classbdm_1_1egamma.html#0901ec983e66b8337aaa506e13b122fa" title="Vector .">alpha</a>;
+<a name="l00317"></a><a class="code" href="classbdm_1_1egamma.html#457bfb1ccb2057df85073e519a15ccc1">00317</a>         vec <a class="code" href="classbdm_1_1egamma.html#457bfb1ccb2057df85073e519a15ccc1" title="Vector .">beta</a>;
+<a name="l00318"></a>00318 <span class="keyword">public</span> :
+<a name="l00320"></a><a class="code" href="classbdm_1_1egamma.html#131197f3dfad99355a01d30903279653">00320</a>         <a class="code" href="classbdm_1_1egamma.html#131197f3dfad99355a01d30903279653" title="Default constructor.">egamma</a> ( ) :<a class="code" href="classbdm_1_1eEF.html" title="General conjugate exponential family posterior density.">eEF</a> ( ), <a class="code" href="classbdm_1_1egamma.html#0901ec983e66b8337aaa506e13b122fa" title="Vector .">alpha</a>(), <a class="code" href="classbdm_1_1egamma.html#457bfb1ccb2057df85073e519a15ccc1" title="Vector .">beta</a>() {};
+<a name="l00322"></a><a class="code" href="classbdm_1_1egamma.html#749f82293ff23a8319c1bf52489d2ed2">00322</a>         <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1egamma.html#749f82293ff23a8319c1bf52489d2ed2" title="Sets parameters.">set_parameters</a> ( <span class="keyword">const</span> vec &amp;a, <span class="keyword">const</span> vec &amp;b ) {<a class="code" href="classbdm_1_1egamma.html#0901ec983e66b8337aaa506e13b122fa" title="Vector .">alpha</a>=a,<a class="code" href="classbdm_1_1egamma.html#457bfb1ccb2057df85073e519a15ccc1" title="Vector .">beta</a>=b;<a class="code" href="classbdm_1_1epdf.html#16adac20ec7fe07e1ea0b27d917788ce" title="dimension of the random variable">dim</a> = <a class="code" href="classbdm_1_1egamma.html#0901ec983e66b8337aaa506e13b122fa" title="Vector .">alpha</a>.length();};
+<a name="l00323"></a>00323         vec <a class="code" href="classbdm_1_1egamma.html#6ed82f0fd05f6002487256d8e75a0bbd" title="Returns a sample,  from density .">sample</a>() <span class="keyword">const</span>;
+<a name="l00325"></a>00325 <span class="comment">//      mat sample ( int N ) const;</span>
+<a name="l00326"></a>00326         <span class="keywordtype">double</span> <a class="code" href="classbdm_1_1egamma.html#a8e11e5a580ff42a1b205974c60768c6" title="TODO: is it used anywhere?">evallog</a> ( <span class="keyword">const</span> vec &amp;val ) <span class="keyword">const</span>;
+<a name="l00327"></a>00327         <span class="keywordtype">double</span> <a class="code" href="classbdm_1_1egamma.html#9a66cbd100e8520c769ccb3c451f86f8" title="logarithm of the normalizing constant, ">lognc</a> () <span class="keyword">const</span>;
+<a name="l00329"></a><a class="code" href="classbdm_1_1egamma.html#0865cb3d6339fdc7410806cf70a329ed">00329</a>         vec&amp; <a class="code" href="classbdm_1_1egamma.html#0865cb3d6339fdc7410806cf70a329ed" title="Returns poiter to alpha and beta. Potentially dengerous: use with care!">_alpha</a>() {<span class="keywordflow">return</span> <a class="code" href="classbdm_1_1egamma.html#0901ec983e66b8337aaa506e13b122fa" title="Vector .">alpha</a>;}
+<a name="l00330"></a>00330         vec&amp; _beta() {<span class="keywordflow">return</span> <a class="code" href="classbdm_1_1egamma.html#457bfb1ccb2057df85073e519a15ccc1" title="Vector .">beta</a>;}
+<a name="l00331"></a><a class="code" href="classbdm_1_1egamma.html#49d256c42cce14c6faa56ec242b57e85">00331</a>         vec <a class="code" href="classbdm_1_1egamma.html#49d256c42cce14c6faa56ec242b57e85" title="return expected value">mean</a>()<span class="keyword"> const </span>{<span class="keywordflow">return</span> elem_div ( <a class="code" href="classbdm_1_1egamma.html#0901ec983e66b8337aaa506e13b122fa" title="Vector .">alpha</a>,<a class="code" href="classbdm_1_1egamma.html#457bfb1ccb2057df85073e519a15ccc1" title="Vector .">beta</a> );}
+<a name="l00332"></a><a class="code" href="classbdm_1_1egamma.html#36986cc01917cd0796fadc17125bdec1">00332</a>         vec <a class="code" href="classbdm_1_1egamma.html#36986cc01917cd0796fadc17125bdec1" title="return expected variance (not covariance!)">variance</a>()<span class="keyword"> const </span>{<span class="keywordflow">return</span> elem_div ( <a class="code" href="classbdm_1_1egamma.html#0901ec983e66b8337aaa506e13b122fa" title="Vector .">alpha</a>,elem_mult ( <a class="code" href="classbdm_1_1egamma.html#457bfb1ccb2057df85073e519a15ccc1" title="Vector .">beta</a>,<a class="code" href="classbdm_1_1egamma.html#457bfb1ccb2057df85073e519a15ccc1" title="Vector .">beta</a> ) ); }
+<a name="l00333"></a>00333 };
+<a name="l00334"></a>00334 
+<a name="l00349"></a><a class="code" href="classbdm_1_1eigamma.html">00349</a> <span class="keyword">class </span><a class="code" href="classbdm_1_1eigamma.html" title="Inverse-Gamma posterior density.">eigamma</a> : <span class="keyword">public</span> <a class="code" href="classbdm_1_1eEF.html" title="General conjugate exponential family posterior density.">eEF</a> {
+<a name="l00350"></a>00350 <span class="keyword">protected</span>:
+<a name="l00352"></a><a class="code" href="classbdm_1_1eigamma.html#3e5c03201f073033a7db894fa15ddb96">00352</a>         <a class="code" href="classbdm_1_1egamma.html" title="Gamma posterior density.">egamma</a> <a class="code" href="classbdm_1_1eigamma.html#3e5c03201f073033a7db894fa15ddb96" title="internal egamma">eg</a>;
+<a name="l00354"></a><a class="code" href="classbdm_1_1eigamma.html#494fafd76d9448395efe160c9ba11ab6">00354</a>         vec &amp;<a class="code" href="classbdm_1_1eigamma.html#494fafd76d9448395efe160c9ba11ab6" title="Vector .">alpha</a>;
+<a name="l00356"></a><a class="code" href="classbdm_1_1eigamma.html#e32ff12ba6b2fdc22068c35dace23fa6">00356</a>         vec &amp;<a class="code" href="classbdm_1_1eigamma.html#e32ff12ba6b2fdc22068c35dace23fa6" title="Vector  (in fact it is 1/beta as used in definition of iG).">beta</a>;
+<a name="l00357"></a>00357 <span class="keyword">public</span> :
+<a name="l00359"></a><a class="code" href="classbdm_1_1eigamma.html#b496396f3511ce87d7ae3830ba94262c">00359</a>         <a class="code" href="classbdm_1_1eigamma.html#b496396f3511ce87d7ae3830ba94262c" title="Default constructor.">eigamma</a> ( ) :<a class="code" href="classbdm_1_1eEF.html" title="General conjugate exponential family posterior density.">eEF</a> ( ), <a class="code" href="classbdm_1_1eigamma.html#3e5c03201f073033a7db894fa15ddb96" title="internal egamma">eg</a>(),<a class="code" href="classbdm_1_1eigamma.html#494fafd76d9448395efe160c9ba11ab6" title="Vector .">alpha</a> ( <a class="code" href="classbdm_1_1eigamma.html#3e5c03201f073033a7db894fa15ddb96" title="internal egamma">eg</a>._alpha() ), <a class="code" href="classbdm_1_1eigamma.html#e32ff12ba6b2fdc22068c35dace23fa6" title="Vector  (in fact it is 1/beta as used in definition of iG).">beta</a> ( <a class="code" href="classbdm_1_1eigamma.html#3e5c03201f073033a7db894fa15ddb96" title="internal egamma">eg</a>._beta() ) {};
+<a name="l00361"></a><a class="code" href="classbdm_1_1eigamma.html#09e616c95f31acddf7dfef96d1c5d645">00361</a>         <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1eigamma.html#09e616c95f31acddf7dfef96d1c5d645" title="Sets parameters.">set_parameters</a> ( <span class="keyword">const</span> vec &amp;a, <span class="keyword">const</span> vec &amp;b ) {<a class="code" href="classbdm_1_1eigamma.html#494fafd76d9448395efe160c9ba11ab6" title="Vector .">alpha</a>=a,<a class="code" href="classbdm_1_1eigamma.html#e32ff12ba6b2fdc22068c35dace23fa6" title="Vector  (in fact it is 1/beta as used in definition of iG).">beta</a>=b;};
+<a name="l00362"></a><a class="code" href="classbdm_1_1eigamma.html#3aff7bf25ddac27731c60826fcfd878f">00362</a>         vec <a class="code" href="classbdm_1_1eigamma.html#3aff7bf25ddac27731c60826fcfd878f" title="Returns a sample,  from density .">sample</a>()<span class="keyword"> const </span>{<span class="keywordflow">return</span> 1.0/<a class="code" href="classbdm_1_1eigamma.html#3e5c03201f073033a7db894fa15ddb96" title="internal egamma">eg</a>.<a class="code" href="classbdm_1_1egamma.html#6ed82f0fd05f6002487256d8e75a0bbd" title="Returns a sample,  from density .">sample</a>();};
+<a name="l00364"></a>00364 <span class="comment">//      mat sample ( int N ) const;</span>
+<a name="l00365"></a><a class="code" href="classbdm_1_1eigamma.html#9e26c80c8e6708bfcf2e684958af6f91">00365</a>         <span class="keywordtype">double</span> <a class="code" href="classbdm_1_1eigamma.html#9e26c80c8e6708bfcf2e684958af6f91" title="TODO: is it used anywhere?">evallog</a> ( <span class="keyword">const</span> vec &amp;val )<span class="keyword"> const </span>{<span class="keywordflow">return</span> <a class="code" href="classbdm_1_1eigamma.html#3e5c03201f073033a7db894fa15ddb96" title="internal egamma">eg</a>.<a class="code" href="classbdm_1_1egamma.html#a8e11e5a580ff42a1b205974c60768c6" title="TODO: is it used anywhere?">evallog</a> ( val );};
+<a name="l00366"></a><a class="code" href="classbdm_1_1eigamma.html#a52ac6d523e2fe05642d1f50fe66aec2">00366</a>         <span class="keywordtype">double</span> <a class="code" href="classbdm_1_1eigamma.html#a52ac6d523e2fe05642d1f50fe66aec2" title="logarithm of the normalizing constant, ">lognc</a> ()<span class="keyword"> const </span>{<span class="keywordflow">return</span> <a class="code" href="classbdm_1_1eigamma.html#3e5c03201f073033a7db894fa15ddb96" title="internal egamma">eg</a>.<a class="code" href="classbdm_1_1egamma.html#9a66cbd100e8520c769ccb3c451f86f8" title="logarithm of the normalizing constant, ">lognc</a>();};
+<a name="l00368"></a><a class="code" href="classbdm_1_1eigamma.html#46cecb295edbabd28120cb0f6f572bcb">00368</a>         vec <a class="code" href="classbdm_1_1eigamma.html#46cecb295edbabd28120cb0f6f572bcb" title="Returns poiter to alpha and beta. Potentially dangerous: use with care!">mean</a>()<span class="keyword"> const </span>{<span class="keywordflow">return</span> elem_div ( <a class="code" href="classbdm_1_1eigamma.html#e32ff12ba6b2fdc22068c35dace23fa6" title="Vector  (in fact it is 1/beta as used in definition of iG).">beta</a>,<a class="code" href="classbdm_1_1eigamma.html#494fafd76d9448395efe160c9ba11ab6" title="Vector .">alpha</a>-1 );}
+<a name="l00369"></a><a class="code" href="classbdm_1_1eigamma.html#c2c696f8c668e9f65392c9449f6a5133">00369</a>         vec <a class="code" href="classbdm_1_1eigamma.html#c2c696f8c668e9f65392c9449f6a5133" title="return expected variance (not covariance!)">variance</a>()<span class="keyword"> const </span>{vec mea=<a class="code" href="classbdm_1_1eigamma.html#46cecb295edbabd28120cb0f6f572bcb" title="Returns poiter to alpha and beta. Potentially dangerous: use with care!">mean</a>(); <span class="keywordflow">return</span> elem_div ( elem_mult ( mea,mea ),<a class="code" href="classbdm_1_1eigamma.html#494fafd76d9448395efe160c9ba11ab6" title="Vector .">alpha</a>-2 );}
+<a name="l00370"></a>00370         vec&amp; _alpha() {<span class="keywordflow">return</span> <a class="code" href="classbdm_1_1eigamma.html#494fafd76d9448395efe160c9ba11ab6" title="Vector .">alpha</a>;}
+<a name="l00371"></a>00371         vec&amp; _beta() {<span class="keywordflow">return</span> <a class="code" href="classbdm_1_1eigamma.html#e32ff12ba6b2fdc22068c35dace23fa6" title="Vector  (in fact it is 1/beta as used in definition of iG).">beta</a>;}
+<a name="l00372"></a>00372 };
+<a name="l00373"></a>00373 <span class="comment">/*</span>
+<a name="l00375"></a>00375 <span class="comment">class emix : public epdf {</span>
+<a name="l00376"></a>00376 <span class="comment">protected:</span>
+<a name="l00377"></a>00377 <span class="comment">        int n;</span>
+<a name="l00378"></a>00378 <span class="comment">        vec &amp;w;</span>
+<a name="l00379"></a>00379 <span class="comment">        Array&lt;epdf*&gt; Coms;</span>
+<a name="l00380"></a>00380 <span class="comment">public:</span>
+<a name="l00382"></a>00382 <span class="comment">        emix ( const RV &amp;rv, vec &amp;w0): epdf(rv), n(w0.length()), w(w0), Coms(n) {};</span>
+<a name="l00383"></a>00383 <span class="comment">        void set_parameters( int &amp;i, double wi, epdf* ep){w(i)=wi;Coms(i)=ep;}</span>
+<a name="l00384"></a>00384 <span class="comment">        vec mean(){vec pom; for(int i=0;i&lt;n;i++){pom+=Coms(i)-&gt;mean()*w(i);} return pom;};</span>
+<a name="l00385"></a>00385 <span class="comment">        vec sample() {it_error ( "Not implemented" );return 0;}</span>
+<a name="l00386"></a>00386 <span class="comment">};</span>
+<a name="l00387"></a>00387 <span class="comment">*/</span>
+<a name="l00388"></a>00388 
 <a name="l00390"></a>00390 
-<a name="l00392"></a>00392 
-<a name="l00393"></a><a class="code" href="classbdm_1_1euni.html">00393</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="l00394"></a>00394 <span class="keyword">protected</span>:
-<a name="l00396"></a><a class="code" href="classbdm_1_1euni.html#ff7ce6a2ef5ef0015bbd1398bed12f32">00396</a>         vec <a class="code" href="classbdm_1_1euni.html#ff7ce6a2ef5ef0015bbd1398bed12f32" title="lower bound on support">low</a>;
-<a name="l00398"></a><a class="code" href="classbdm_1_1euni.html#cfad2dea4a62db6872bda8abd75f0de1">00398</a>         vec <a class="code" href="classbdm_1_1euni.html#cfad2dea4a62db6872bda8abd75f0de1" title="upper bound on support">high</a>;
-<a name="l00400"></a><a class="code" href="classbdm_1_1euni.html#d3c27e331f90c754d80228108de8ed4c">00400</a>         vec <a class="code" href="classbdm_1_1euni.html#d3c27e331f90c754d80228108de8ed4c" title="internal">distance</a>;
-<a name="l00402"></a><a class="code" href="classbdm_1_1euni.html#31bb13e8449a8eff35246d46dae35c20">00402</a>         <span class="keywordtype">double</span> <a class="code" href="classbdm_1_1euni.html#31bb13e8449a8eff35246d46dae35c20" title="normalizing coefficients">nk</a>;
-<a name="l00404"></a><a class="code" href="classbdm_1_1euni.html#3e63be48dd58659663ca60cd18700476">00404</a>         <span class="keywordtype">double</span> <a class="code" href="classbdm_1_1euni.html#3e63be48dd58659663ca60cd18700476" title="cache of log( nk )">lnk</a>;
-<a name="l00405"></a>00405 <span class="keyword">public</span>:
-<a name="l00407"></a><a class="code" href="classbdm_1_1euni.html#dca02eda833d6295e0c19f6e120b64e0">00407</a>         <a class="code" href="classbdm_1_1euni.html#dca02eda833d6295e0c19f6e120b64e0" title="Defualt constructor.">euni</a> ( <span class="keyword">const</span> <a class="code" href="classbdm_1_1RV.html" title="Class representing variables, most often random variables.">RV</a> <a class="code" href="classbdm_1_1epdf.html#62c5b8ff71d9ebe6cd58d3c342eb1dc8" title="Identified of the random variable.">rv</a> ) :<a class="code" href="classbdm_1_1epdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a> ( rv ) {}
-<a name="l00408"></a>00408         <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="l00409"></a><a class="code" href="classbdm_1_1euni.html#caa07b8307bd793d5339d6583e0aba81">00409</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="l00410"></a><a class="code" href="classbdm_1_1euni.html#fc5df80359ead2918384b2004ce67194">00410</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="l00411"></a>00411                 vec smp ( <a class="code" href="classbdm_1_1epdf.html#62c5b8ff71d9ebe6cd58d3c342eb1dc8" title="Identified of the random variable.">rv</a>.<a class="code" href="classbdm_1_1RV.html#2174751a00ce19f941edd2c1a861be67" title="Return number of scalars in the RV.">count</a>() );
-<a name="l00412"></a>00412 <span class="preprocessor">#pragma omp critical</span>
-<a name="l00413"></a>00413 <span class="preprocessor"></span>                <a class="code" href="namespacebdm.html#96288dbda6916cd442af735f66a9f40b" title="Global Uniform_RNG.">UniRNG</a>.sample_vector ( <a class="code" href="classbdm_1_1epdf.html#62c5b8ff71d9ebe6cd58d3c342eb1dc8" title="Identified of the random variable.">rv</a>.<a class="code" href="classbdm_1_1RV.html#2174751a00ce19f941edd2c1a861be67" title="Return number of scalars in the RV.">count</a>(),smp );
-<a name="l00414"></a>00414                 <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="l00415"></a>00415         }
-<a name="l00417"></a><a class="code" href="classbdm_1_1euni.html#8e130b323c62b42f1699537f99af6f09">00417</a>         <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1euni.html#8e130b323c62b42f1699537f99af6f09" title="set values of low and high ">set_parameters</a> ( <span class="keyword">const</span> vec &amp;low0, <span class="keyword">const</span> vec &amp;high0 ) {
-<a name="l00418"></a>00418                 <a class="code" href="classbdm_1_1euni.html#d3c27e331f90c754d80228108de8ed4c" title="internal">distance</a> = high0-low0;
-<a name="l00419"></a>00419                 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="l00420"></a>00420                 <a class="code" href="classbdm_1_1euni.html#ff7ce6a2ef5ef0015bbd1398bed12f32" title="lower bound on support">low</a> = low0;
-<a name="l00421"></a>00421                 <a class="code" href="classbdm_1_1euni.html#cfad2dea4a62db6872bda8abd75f0de1" title="upper bound on support">high</a> = high0;
-<a name="l00422"></a>00422                 <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="l00423"></a>00423                 <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="l00424"></a>00424         }
-<a name="l00425"></a><a class="code" href="classbdm_1_1euni.html#46caa8c13aba2e6228f964208918b226">00425</a>         vec <a class="code" href="classbdm_1_1euni.html#46caa8c13aba2e6228f964208918b226" title="return expected value">mean</a>()<span class="keyword"> const </span>{<span class="keywordflow">return</span> (<a class="code" href="classbdm_1_1euni.html#cfad2dea4a62db6872bda8abd75f0de1" title="upper bound on support">high</a>-<a class="code" href="classbdm_1_1euni.html#ff7ce6a2ef5ef0015bbd1398bed12f32" title="lower bound on support">low</a>)/2.0;}
-<a name="l00426"></a><a class="code" href="classbdm_1_1euni.html#951f932155111f6053c980f672b4c22c">00426</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="l00427"></a>00427 };
+<a name="l00391"></a><a class="code" href="classbdm_1_1euni.html">00391</a> <span class="keyword">class </span><a class="code" href="classbdm_1_1euni.html" title="Uniform distributed density on a rectangular support.">euni</a>: <span class="keyword">public</span> <a class="code" href="classbdm_1_1epdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a> {
+<a name="l00392"></a>00392 <span class="keyword">protected</span>:
+<a name="l00394"></a><a class="code" href="classbdm_1_1euni.html#ff7ce6a2ef5ef0015bbd1398bed12f32">00394</a>         vec <a class="code" href="classbdm_1_1euni.html#ff7ce6a2ef5ef0015bbd1398bed12f32" title="lower bound on support">low</a>;
+<a name="l00396"></a><a class="code" href="classbdm_1_1euni.html#cfad2dea4a62db6872bda8abd75f0de1">00396</a>         vec <a class="code" href="classbdm_1_1euni.html#cfad2dea4a62db6872bda8abd75f0de1" title="upper bound on support">high</a>;
+<a name="l00398"></a><a class="code" href="classbdm_1_1euni.html#d3c27e331f90c754d80228108de8ed4c">00398</a>         vec <a class="code" href="classbdm_1_1euni.html#d3c27e331f90c754d80228108de8ed4c" title="internal">distance</a>;
+<a name="l00400"></a><a class="code" href="classbdm_1_1euni.html#31bb13e8449a8eff35246d46dae35c20">00400</a>         <span class="keywordtype">double</span> <a class="code" href="classbdm_1_1euni.html#31bb13e8449a8eff35246d46dae35c20" title="normalizing coefficients">nk</a>;
+<a name="l00402"></a><a class="code" href="classbdm_1_1euni.html#3e63be48dd58659663ca60cd18700476">00402</a>         <span class="keywordtype">double</span> <a class="code" href="classbdm_1_1euni.html#3e63be48dd58659663ca60cd18700476" title="cache of log( nk )">lnk</a>;
+<a name="l00403"></a>00403 <span class="keyword">public</span>:
+<a name="l00405"></a><a class="code" href="classbdm_1_1euni.html#8887b088e3ab0b01e96b88237d707b97">00405</a>         <a class="code" href="classbdm_1_1euni.html#8887b088e3ab0b01e96b88237d707b97" title="Defualt constructor.">euni</a> ( ) :<a class="code" href="classbdm_1_1epdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a> ( ) {}
+<a name="l00406"></a>00406         <span class="keywordtype">double</span> eval ( <span class="keyword">const</span> vec &amp;val )<span class="keyword"> const  </span>{<span class="keywordflow">return</span> <a class="code" href="classbdm_1_1euni.html#31bb13e8449a8eff35246d46dae35c20" title="normalizing coefficients">nk</a>;}
+<a name="l00407"></a><a class="code" href="classbdm_1_1euni.html#caa07b8307bd793d5339d6583e0aba81">00407</a>         <span class="keywordtype">double</span> <a class="code" href="classbdm_1_1euni.html#caa07b8307bd793d5339d6583e0aba81" title="Compute log-probability of argument val.">evallog</a> ( <span class="keyword">const</span> vec &amp;val )<span class="keyword"> const  </span>{<span class="keywordflow">return</span> <a class="code" href="classbdm_1_1euni.html#3e63be48dd58659663ca60cd18700476" title="cache of log( nk )">lnk</a>;}
+<a name="l00408"></a><a class="code" href="classbdm_1_1euni.html#fc5df80359ead2918384b2004ce67194">00408</a>         vec <a class="code" href="classbdm_1_1euni.html#fc5df80359ead2918384b2004ce67194" title="Returns a sample,  from density .">sample</a>()<span class="keyword"> const </span>{
+<a name="l00409"></a>00409                 vec smp ( <a class="code" href="classbdm_1_1epdf.html#16adac20ec7fe07e1ea0b27d917788ce" title="dimension of the random variable">dim</a> );
+<a name="l00410"></a>00410 <span class="preprocessor">#pragma omp critical</span>
+<a name="l00411"></a>00411 <span class="preprocessor"></span>                <a class="code" href="namespacebdm.html#96288dbda6916cd442af735f66a9f40b" title="Global Uniform_RNG.">UniRNG</a>.sample_vector ( <a class="code" href="classbdm_1_1epdf.html#16adac20ec7fe07e1ea0b27d917788ce" title="dimension of the random variable">dim</a> ,smp );
+<a name="l00412"></a>00412                 <span class="keywordflow">return</span> <a class="code" href="classbdm_1_1euni.html#ff7ce6a2ef5ef0015bbd1398bed12f32" title="lower bound on support">low</a>+elem_mult ( <a class="code" href="classbdm_1_1euni.html#d3c27e331f90c754d80228108de8ed4c" title="internal">distance</a>,smp );
+<a name="l00413"></a>00413         }
+<a name="l00415"></a><a class="code" href="classbdm_1_1euni.html#8e130b323c62b42f1699537f99af6f09">00415</a>         <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1euni.html#8e130b323c62b42f1699537f99af6f09" title="set values of low and high ">set_parameters</a> ( <span class="keyword">const</span> vec &amp;low0, <span class="keyword">const</span> vec &amp;high0 ) {
+<a name="l00416"></a>00416                 <a class="code" href="classbdm_1_1euni.html#d3c27e331f90c754d80228108de8ed4c" title="internal">distance</a> = high0-low0;
+<a name="l00417"></a>00417                 it_assert_debug ( min ( <a class="code" href="classbdm_1_1euni.html#d3c27e331f90c754d80228108de8ed4c" title="internal">distance</a> ) &gt;0.0,<span class="stringliteral">"bad support"</span> );
+<a name="l00418"></a>00418                 <a class="code" href="classbdm_1_1euni.html#ff7ce6a2ef5ef0015bbd1398bed12f32" title="lower bound on support">low</a> = low0;
+<a name="l00419"></a>00419                 <a class="code" href="classbdm_1_1euni.html#cfad2dea4a62db6872bda8abd75f0de1" title="upper bound on support">high</a> = high0;
+<a name="l00420"></a>00420                 <a class="code" href="classbdm_1_1euni.html#31bb13e8449a8eff35246d46dae35c20" title="normalizing coefficients">nk</a> = prod ( 1.0/<a class="code" href="classbdm_1_1euni.html#d3c27e331f90c754d80228108de8ed4c" title="internal">distance</a> );
+<a name="l00421"></a>00421                 <a class="code" href="classbdm_1_1euni.html#3e63be48dd58659663ca60cd18700476" title="cache of log( nk )">lnk</a> = log ( <a class="code" href="classbdm_1_1euni.html#31bb13e8449a8eff35246d46dae35c20" title="normalizing coefficients">nk</a> );
+<a name="l00422"></a>00422                 <a class="code" href="classbdm_1_1epdf.html#16adac20ec7fe07e1ea0b27d917788ce" title="dimension of the random variable">dim</a> = <a class="code" href="classbdm_1_1euni.html#ff7ce6a2ef5ef0015bbd1398bed12f32" title="lower bound on support">low</a>.length();
+<a name="l00423"></a>00423         }
+<a name="l00424"></a><a class="code" href="classbdm_1_1euni.html#46caa8c13aba2e6228f964208918b226">00424</a>         vec <a class="code" href="classbdm_1_1euni.html#46caa8c13aba2e6228f964208918b226" title="return expected value">mean</a>()<span class="keyword"> const </span>{<span class="keywordflow">return</span> ( <a class="code" href="classbdm_1_1euni.html#cfad2dea4a62db6872bda8abd75f0de1" title="upper bound on support">high</a>-<a class="code" href="classbdm_1_1euni.html#ff7ce6a2ef5ef0015bbd1398bed12f32" title="lower bound on support">low</a> ) /2.0;}
+<a name="l00425"></a><a class="code" href="classbdm_1_1euni.html#951f932155111f6053c980f672b4c22c">00425</a>         vec <a class="code" href="classbdm_1_1euni.html#951f932155111f6053c980f672b4c22c" title="return expected variance (not covariance!)">variance</a>()<span class="keyword"> const </span>{<span class="keywordflow">return</span> ( pow ( <a class="code" href="classbdm_1_1euni.html#cfad2dea4a62db6872bda8abd75f0de1" title="upper bound on support">high</a>,2 ) +pow ( <a class="code" href="classbdm_1_1euni.html#ff7ce6a2ef5ef0015bbd1398bed12f32" title="lower bound on support">low</a>,2 ) +elem_mult ( <a class="code" href="classbdm_1_1euni.html#cfad2dea4a62db6872bda8abd75f0de1" title="upper bound on support">high</a>,<a class="code" href="classbdm_1_1euni.html#ff7ce6a2ef5ef0015bbd1398bed12f32" title="lower bound on support">low</a> ) ) /3.0;}
+<a name="l00426"></a>00426 };
+<a name="l00427"></a>00427 
 <a name="l00428"></a>00428 
-<a name="l00429"></a>00429 
-<a name="l00435"></a>00435 <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt;
-<a name="l00436"></a><a class="code" href="classbdm_1_1mlnorm.html">00436</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="l00437"></a>00437 <span class="keyword">protected</span>:
-<a name="l00439"></a><a class="code" href="classbdm_1_1mlnorm.html#150ad6acb223b0a0abeaf92346686dcd">00439</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="l00440"></a>00440         mat A;
-<a name="l00441"></a>00441         vec mu_const;
-<a name="l00442"></a>00442         vec&amp; _mu; <span class="comment">//cached epdf.mu;</span>
-<a name="l00443"></a>00443 <span class="keyword">public</span>:
-<a name="l00445"></a>00445         <a class="code" href="classbdm_1_1mlnorm.html#64d965df6811ff65b94718c427048f4a" title="Constructor.">mlnorm</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_1mpdf.html#9bcfb45435d30983f436d41c298cbb51" title="modeled random variable">rv</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_1mpdf.html#5a5f08950daa08b85b01ddf4e1c36288" title="random variable in condition">rvc</a> );
-<a name="l00447"></a>00447         <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="l00448"></a>00448 <span class="comment">//      //!Generate one sample of the posterior</span>
-<a name="l00449"></a>00449 <span class="comment">//      vec samplecond (const vec &amp;cond, double &amp;lik );</span>
-<a name="l00450"></a>00450 <span class="comment">//      //!Generate matrix of samples of the posterior</span>
-<a name="l00451"></a>00451 <span class="comment">//      mat samplecond (const vec &amp;cond, vec &amp;lik, int n );</span>
-<a name="l00453"></a>00453 <span class="comment"></span>        <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1mlnorm.html#0dafc0196e7e07fd06dc6716e0e18bc2" title="Set value of rvc . Result of this operation is stored in epdf use function _ep to...">condition</a> ( <span class="keyword">const</span> vec &amp;cond );
-<a name="l00454"></a>00454 
-<a name="l00456"></a><a class="code" href="classbdm_1_1mlnorm.html#56e587952f94fcac6cfc999eae6dbced">00456</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="l00458"></a><a class="code" href="classbdm_1_1mlnorm.html#262a2a486bff585f34bb6a5005b02614">00458</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="l00460"></a><a class="code" href="classbdm_1_1mlnorm.html#78120ecd1c2b1d7e80124b4603504604">00460</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="l00461"></a>00461 
-<a name="l00462"></a>00462         <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_M&gt;
-<a name="l00463"></a>00463         <span class="keyword">friend</span> std::ostream &amp;operator&lt;&lt; ( std::ostream &amp;os,  mlnorm&lt;sq_M&gt; &amp;ml );
-<a name="l00464"></a>00464 };
-<a name="l00465"></a>00465 
-<a name="l00473"></a><a class="code" href="classbdm_1_1mlstudent.html">00473</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="l00474"></a>00474 <span class="keyword">protected</span>:
-<a name="l00475"></a>00475         <a class="code" href="classldmat.html" title="Matrix stored in LD form, (commonly known as UD).">ldmat</a> Lambda;
-<a name="l00476"></a>00476         <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="l00477"></a>00477         <a class="code" href="classldmat.html" title="Matrix stored in LD form, (commonly known as UD).">ldmat</a> Re;
-<a name="l00478"></a>00478 <span class="keyword">public</span>:
-<a name="l00479"></a>00479         <a class="code" href="classbdm_1_1mlstudent.html">mlstudent</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;rv0, <span class="keyword">const</span> <a class="code" href="classbdm_1_1RV.html" title="Class representing variables, most often random variables.">RV</a> &amp;rvc0 ) :<a class="code" href="classbdm_1_1mlnorm.html" title="Normal distributed linear function with linear function of mean value;.">mlnorm&lt;ldmat&gt;</a> ( rv0,rvc0 ),
-<a name="l00480"></a>00480                         Lambda ( rv0.<a class="code" href="classbdm_1_1RV.html#2174751a00ce19f941edd2c1a861be67" title="Return number of scalars in the RV.">count</a>() ),
-<a name="l00481"></a>00481                         <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="l00482"></a>00482         <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="l00483"></a>00483                 <a class="code" href="classbdm_1_1epdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a>.set_parameters ( zeros ( <a class="code" href="classbdm_1_1mpdf.html#9bcfb45435d30983f436d41c298cbb51" title="modeled random variable">rv</a>.<a class="code" href="classbdm_1_1RV.html#2174751a00ce19f941edd2c1a861be67" title="Return number of scalars in the RV.">count</a>() ),Lambda );
-<a name="l00484"></a>00484                 A = A0;
-<a name="l00485"></a>00485                 mu_const = mu0;
-<a name="l00486"></a>00486                 Re=R0;
-<a name="l00487"></a>00487                 Lambda = Lambda0;
-<a name="l00488"></a>00488         }
-<a name="l00489"></a><a class="code" href="classbdm_1_1mlstudent.html#efd37560585c8613897f30d3c2f58d0d">00489</a>         <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1mlstudent.html#efd37560585c8613897f30d3c2f58d0d" title="Set value of rvc . Result of this operation is stored in epdf use function _ep to...">condition</a> ( <span class="keyword">const</span> vec &amp;cond ) {
-<a name="l00490"></a>00490                 _mu = A*cond + mu_const;
-<a name="l00491"></a>00491                 <span class="keywordtype">double</span> zeta;
-<a name="l00492"></a>00492                 <span class="comment">//ugly hack!</span>
-<a name="l00493"></a>00493                 <span class="keywordflow">if</span> ((cond.length()+1)==Lambda.<a class="code" href="group__math.html#g96dfb21865db4f5bd36fa70f9b0b1163" title="access function">rows</a>()){
-<a name="l00494"></a>00494                         zeta = Lambda.<a class="code" href="classldmat.html#d876c5f83e02b3e809b35c9de5068f14" title="Evaluates quadratic form ;.">invqform</a> ( <a class="code" href="namespacebdm.html#b9016687c0e874ca5cdcf75ae28811aa" title="Concat two random variables.">concat</a>(cond, vec_1(1.0)) );
-<a name="l00495"></a>00495                 } <span class="keywordflow">else</span> {
-<a name="l00496"></a>00496                         zeta = Lambda.<a class="code" href="classldmat.html#d876c5f83e02b3e809b35c9de5068f14" title="Evaluates quadratic form ;.">invqform</a> ( cond );
-<a name="l00497"></a>00497                 }
-<a name="l00498"></a>00498                 <a class="code" href="classbdm_1_1mlnorm.html#78120ecd1c2b1d7e80124b4603504604" title="access function">_R</a> = Re;
-<a name="l00499"></a>00499                 <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="l00500"></a>00500         };
-<a name="l00501"></a>00501 
-<a name="l00502"></a>00502 };
-<a name="l00512"></a><a class="code" href="classbdm_1_1mgamma.html">00512</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="l00513"></a>00513 <span class="keyword">protected</span>:
-<a name="l00515"></a><a class="code" href="classbdm_1_1mgamma.html#bdc9f1e9e03c09e91103fee269864438">00515</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="l00517"></a><a class="code" href="classbdm_1_1mgamma.html#b20cf88cca1fe9b0b8f2a412608bfd09">00517</a>         <span class="keywordtype">double</span> <a class="code" href="classbdm_1_1mgamma.html#b20cf88cca1fe9b0b8f2a412608bfd09" title="Constant .">k</a>;
-<a name="l00519"></a><a class="code" href="classbdm_1_1mgamma.html#f6a652ce70fa2eb4d2c7ba6d5a6ae343">00519</a>         vec* <a class="code" href="classbdm_1_1mgamma.html#f6a652ce70fa2eb4d2c7ba6d5a6ae343" title="cache of epdf.beta">_beta</a>;
-<a name="l00520"></a>00520 
-<a name="l00521"></a>00521 <span class="keyword">public</span>:
-<a name="l00523"></a><a class="code" href="classbdm_1_1mgamma.html#2f6425cd966191b0be4c6ea91a40b6d9">00523</a>         <a class="code" href="classbdm_1_1mgamma.html#2f6425cd966191b0be4c6ea91a40b6d9" title="Constructor.">mgamma</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_1mpdf.html#9bcfb45435d30983f436d41c298cbb51" title="modeled random variable">rv</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_1mpdf.html#5a5f08950daa08b85b01ddf4e1c36288" title="random variable in condition">rvc</a> ): <a class="code" href="classbdm_1_1mEF.html" title="Exponential family model.">mEF</a> ( rv,rvc ), <a class="code" href="classbdm_1_1epdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a> ( rv ) {vec* tmp; <a class="code" href="classbdm_1_1epdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a>._param ( tmp,<a class="code" href="classbdm_1_1mgamma.html#f6a652ce70fa2eb4d2c7ba6d5a6ae343" 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="l00525"></a>00525         <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1mgamma.html#0b486f7e52a3d8a39adbcbd325461c0d" title="Set value of k.">set_parameters</a> ( <span class="keywordtype">double</span> <a class="code" href="classbdm_1_1mgamma.html#b20cf88cca1fe9b0b8f2a412608bfd09" title="Constant .">k</a> );
-<a name="l00526"></a><a class="code" href="classbdm_1_1mgamma.html#8996500f1885e39cde30221b20900bff">00526</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#f6a652ce70fa2eb4d2c7ba6d5a6ae343" title="cache of epdf.beta">_beta</a>=<a class="code" href="classbdm_1_1mgamma.html#b20cf88cca1fe9b0b8f2a412608bfd09" title="Constant .">k</a>/val;};
-<a name="l00527"></a>00527 };
-<a name="l00528"></a>00528 
-<a name="l00538"></a><a class="code" href="classbdm_1_1migamma.html">00538</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="l00539"></a>00539         <span class="keyword">protected</span>:
-<a name="l00541"></a><a class="code" href="classbdm_1_1migamma.html#a31b39d4179551b593c9e0d7d756783a">00541</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="l00543"></a><a class="code" href="classbdm_1_1migamma.html#dc56bc9da542e0103ec16b9be8e5e38c">00543</a>                 <span class="keywordtype">double</span> <a class="code" href="classbdm_1_1migamma.html#dc56bc9da542e0103ec16b9be8e5e38c" title="Constant .">k</a>;
-<a name="l00545"></a><a class="code" href="classbdm_1_1migamma.html#4825c0ef11a148bad9b802a496f56f96">00545</a>                 vec* <a class="code" href="classbdm_1_1migamma.html#4825c0ef11a148bad9b802a496f56f96" title="cache of epdf.beta">_beta</a>;
-<a name="l00547"></a><a class="code" href="classbdm_1_1migamma.html#b6c265b132ff79963bf51dff4c3ef252">00547</a>                 vec* <a class="code" href="classbdm_1_1migamma.html#b6c265b132ff79963bf51dff4c3ef252" title="chaceh of epdf.alpha">_alpha</a>;
-<a name="l00548"></a>00548 
-<a name="l00549"></a>00549         <span class="keyword">public</span>:
-<a name="l00551"></a><a class="code" href="classbdm_1_1migamma.html#07c5970da0e578ce8a428f1ebf46a459">00551</a>                 <a class="code" href="classbdm_1_1migamma.html#07c5970da0e578ce8a428f1ebf46a459" title="Constructor.">migamma</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_1mpdf.html#9bcfb45435d30983f436d41c298cbb51" title="modeled random variable">rv</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_1mpdf.html#5a5f08950daa08b85b01ddf4e1c36288" title="random variable in condition">rvc</a> ): <a class="code" href="classbdm_1_1mEF.html" title="Exponential family model.">mEF</a> ( rv,rvc ), <a class="code" href="classbdm_1_1epdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a> ( rv ) {<a class="code" href="classbdm_1_1epdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a>._param ( <a class="code" href="classbdm_1_1migamma.html#b6c265b132ff79963bf51dff4c3ef252" title="chaceh of epdf.alpha">_alpha</a>,<a class="code" href="classbdm_1_1migamma.html#4825c0ef11a148bad9b802a496f56f96" 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="l00553"></a><a class="code" href="classbdm_1_1migamma.html#1d7023b1565551d0260eb1ba832bebaf">00553</a>                 <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1migamma.html#1d7023b1565551d0260eb1ba832bebaf" title="Set value of k.">set_parameters</a> ( <span class="keywordtype">double</span> k0 ){<a class="code" href="classbdm_1_1migamma.html#dc56bc9da542e0103ec16b9be8e5e38c" title="Constant .">k</a>=k0;*<a class="code" href="classbdm_1_1migamma.html#b6c265b132ff79963bf51dff4c3ef252" title="chaceh of epdf.alpha">_alpha</a>=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;};
-<a name="l00554"></a><a class="code" href="classbdm_1_1migamma.html#7a34b1e2e3aa2250d7c0ed7df1665b8c">00554</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="l00555"></a>00555                         *<a class="code" href="classbdm_1_1migamma.html#4825c0ef11a148bad9b802a496f56f96" title="cache of epdf.beta">_beta</a>=elem_mult(val,(*<a class="code" href="classbdm_1_1migamma.html#b6c265b132ff79963bf51dff4c3ef252" title="chaceh of epdf.alpha">_alpha</a>-1));
-<a name="l00556"></a>00556                 };
-<a name="l00557"></a>00557 };
-<a name="l00558"></a>00558 
-<a name="l00570"></a><a class="code" href="classbdm_1_1mgamma__fix.html">00570</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="l00571"></a>00571 <span class="keyword">protected</span>:
-<a name="l00573"></a><a class="code" href="classbdm_1_1mgamma__fix.html#1eb701506aabb2e6af007e487212d6fa">00573</a>         <span class="keywordtype">double</span> <a class="code" href="classbdm_1_1mgamma__fix.html#1eb701506aabb2e6af007e487212d6fa" title="parameter l">l</a>;
-<a name="l00575"></a><a class="code" href="classbdm_1_1mgamma__fix.html#018c6f901a04e419455308a07eb3b0b2">00575</a>         vec <a class="code" href="classbdm_1_1mgamma__fix.html#018c6f901a04e419455308a07eb3b0b2" title="reference vector">refl</a>;
-<a name="l00576"></a>00576 <span class="keyword">public</span>:
-<a name="l00578"></a><a class="code" href="classbdm_1_1mgamma__fix.html#c73571f45ab2926e5a7fb9c3791b5614">00578</a>         <a class="code" href="classbdm_1_1mgamma__fix.html#c73571f45ab2926e5a7fb9c3791b5614" title="Constructor.">mgamma_fix</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_1mpdf.html#9bcfb45435d30983f436d41c298cbb51" title="modeled random variable">rv</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_1mpdf.html#5a5f08950daa08b85b01ddf4e1c36288" title="random variable in condition">rvc</a> ) : <a class="code" href="classbdm_1_1mgamma.html" title="Gamma random walk.">mgamma</a> ( rv,rvc ),<a class="code" href="classbdm_1_1mgamma__fix.html#018c6f901a04e419455308a07eb3b0b2" title="reference vector">refl</a> ( rv.count() ) {};
-<a name="l00580"></a><a class="code" href="classbdm_1_1mgamma__fix.html#1bfd30e90db9dc1fbda4a9fbb0b716b2">00580</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="l00581"></a>00581                 <a class="code" href="classbdm_1_1mgamma.html#0b486f7e52a3d8a39adbcbd325461c0d" title="Set value of k.">mgamma::set_parameters</a> ( k0 );
-<a name="l00582"></a>00582                 <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="l00583"></a>00583         };
-<a name="l00584"></a>00584 
-<a name="l00585"></a><a class="code" href="classbdm_1_1mgamma__fix.html#1d539591deb7a38bb3403c2b396c8ff7">00585</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#f6a652ce70fa2eb4d2c7ba6d5a6ae343" title="cache of epdf.beta">_beta</a>=<a class="code" href="classbdm_1_1mgamma.html#b20cf88cca1fe9b0b8f2a412608bfd09" title="Constant .">k</a>/mean;};
-<a name="l00586"></a>00586 };
-<a name="l00587"></a>00587 
-<a name="l00588"></a>00588 
-<a name="l00601"></a><a class="code" href="classbdm_1_1migamma__fix.html">00601</a> <span class="keyword">class </span><a class="code" href="classbdm_1_1migamma__fix.html" title="Inverse-Gamma random walk around a fixed point.">migamma_fix</a> : <span class="keyword">public</span> <a class="code" href="classbdm_1_1migamma.html" title="Inverse-Gamma random walk.">migamma</a> {
-<a name="l00602"></a>00602         <span class="keyword">protected</span>:
-<a name="l00604"></a><a class="code" href="classbdm_1_1migamma__fix.html#e1c344accac36d7ccc3ffa502e8d2f4e">00604</a>                 <span class="keywordtype">double</span> <a class="code" href="classbdm_1_1migamma__fix.html#e1c344accac36d7ccc3ffa502e8d2f4e" title="parameter l">l</a>;
-<a name="l00606"></a><a class="code" href="classbdm_1_1migamma__fix.html#5d453e5a2bdfc9a16c8acb8842dc9780">00606</a>                 vec <a class="code" href="classbdm_1_1migamma__fix.html#5d453e5a2bdfc9a16c8acb8842dc9780" title="reference vector">refl</a>;
-<a name="l00607"></a>00607         <span class="keyword">public</span>:
-<a name="l00609"></a><a class="code" href="classbdm_1_1migamma__fix.html#3c6aacebccbe6d73f8d442e82d3cb53a">00609</a>                 <a class="code" href="classbdm_1_1migamma__fix.html#3c6aacebccbe6d73f8d442e82d3cb53a" title="Constructor.">migamma_fix</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_1mpdf.html#9bcfb45435d30983f436d41c298cbb51" title="modeled random variable">rv</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_1mpdf.html#5a5f08950daa08b85b01ddf4e1c36288" title="random variable in condition">rvc</a> ) : <a class="code" href="classbdm_1_1migamma.html" title="Inverse-Gamma random walk.">migamma</a> ( rv,rvc ),<a class="code" href="classbdm_1_1migamma__fix.html#5d453e5a2bdfc9a16c8acb8842dc9780" title="reference vector">refl</a> ( rv.count() ) {};
-<a name="l00611"></a><a class="code" href="classbdm_1_1migamma__fix.html#17f9ce1068660a4e8c05173bef7d7440">00611</a>                 <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1migamma__fix.html#17f9ce1068660a4e8c05173bef7d7440" title="Set value of k.">set_parameters</a> ( <span class="keywordtype">double</span> k0 , vec ref0, <span class="keywordtype">double</span> l0 ) {
-<a name="l00612"></a>00612                         <a class="code" href="classbdm_1_1migamma.html#1d7023b1565551d0260eb1ba832bebaf" title="Set value of k.">migamma::set_parameters</a> ( k0 );
-<a name="l00613"></a>00613                         <a class="code" href="classbdm_1_1migamma__fix.html#5d453e5a2bdfc9a16c8acb8842dc9780" title="reference vector">refl</a>=pow ( ref0,1.0-l0 );<a class="code" href="classbdm_1_1migamma__fix.html#e1c344accac36d7ccc3ffa502e8d2f4e" title="parameter l">l</a>=l0;
-<a name="l00614"></a>00614                 };
-<a name="l00615"></a>00615 
-<a name="l00616"></a><a class="code" href="classbdm_1_1migamma__fix.html#cfbabd293795d44aae1b7c874e57c4b8">00616</a>                 <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1migamma__fix.html#cfbabd293795d44aae1b7c874e57c4b8" title="Update ep so that it represents this mpdf conditioned on rvc = cond.">condition</a> ( <span class="keyword">const</span> vec &amp;val ) {vec mean=elem_mult ( <a class="code" href="classbdm_1_1migamma__fix.html#5d453e5a2bdfc9a16c8acb8842dc9780" title="reference vector">refl</a>,pow ( val,<a class="code" href="classbdm_1_1migamma__fix.html#e1c344accac36d7ccc3ffa502e8d2f4e" title="parameter l">l</a> ) ); <a class="code" href="classbdm_1_1migamma.html#7a34b1e2e3aa2250d7c0ed7df1665b8c" title="Update ep so that it represents this mpdf conditioned on rvc = cond.">migamma::condition</a>(mean);};
-<a name="l00617"></a>00617 };
-<a name="l00619"></a><a class="code" href="namespacebdm.html#33aac0be76ded31d2e3081c5a3f6c418">00619</a> <span class="keyword">enum</span> <a class="code" href="namespacebdm.html#33aac0be76ded31d2e3081c5a3f6c418" title="Switch between various resampling methods.">RESAMPLING_METHOD</a> { MULTINOMIAL = 0, STRATIFIED = 1, SYSTEMATIC = 3 };
-<a name="l00625"></a><a class="code" href="classbdm_1_1eEmp.html">00625</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="l00626"></a>00626 <span class="keyword">protected</span> :
-<a name="l00628"></a><a class="code" href="classbdm_1_1eEmp.html#9798006271ca77629855113f1283a031">00628</a>         <span class="keywordtype">int</span> <a class="code" href="classbdm_1_1eEmp.html#9798006271ca77629855113f1283a031" title="Number of particles.">n</a>;
-<a name="l00630"></a><a class="code" href="classbdm_1_1eEmp.html#9d39241aab7c4bbeb07c6d516421c67d">00630</a>         vec <a class="code" href="classbdm_1_1eEmp.html#9d39241aab7c4bbeb07c6d516421c67d" title="Sample weights .">w</a>;
-<a name="l00632"></a><a class="code" href="classbdm_1_1eEmp.html#73d819553a0f268b055a087d2d4486f3">00632</a>         Array&lt;vec&gt; <a class="code" href="classbdm_1_1eEmp.html#73d819553a0f268b055a087d2d4486f3" title="Samples .">samples</a>;
-<a name="l00633"></a>00633 <span class="keyword">public</span>:
-<a name="l00635"></a><a class="code" href="classbdm_1_1eEmp.html#47ee4feee19b3f3e2d371f8fc9f9a863">00635</a>         <a class="code" href="classbdm_1_1eEmp.html#47ee4feee19b3f3e2d371f8fc9f9a863" title="Default constructor.">eEmp</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;rv0 ,<span class="keywordtype">int</span> n0 ) :<a class="code" href="classbdm_1_1epdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a> ( rv0 ),<a class="code" href="classbdm_1_1eEmp.html#9798006271ca77629855113f1283a031" title="Number of particles.">n</a> ( n0 ),<a class="code" href="classbdm_1_1eEmp.html#9d39241aab7c4bbeb07c6d516421c67d" title="Sample weights .">w</a> ( <a class="code" href="classbdm_1_1eEmp.html#9798006271ca77629855113f1283a031" title="Number of particles.">n</a> ),<a class="code" href="classbdm_1_1eEmp.html#73d819553a0f268b055a087d2d4486f3" title="Samples .">samples</a> ( <a class="code" href="classbdm_1_1eEmp.html#9798006271ca77629855113f1283a031" title="Number of particles.">n</a> ) {};
-<a name="l00637"></a>00637         <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1eEmp.html#82320074a9b0ad7e1bb33a6e885b65d7" title="Set samples and weights.">set_parameters</a> ( <span class="keyword">const</span> vec &amp;w0, <span class="keyword">const</span> <a class="code" href="classbdm_1_1epdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a>* pdf0 );
-<a name="l00639"></a>00639         <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="l00641"></a><a class="code" href="classbdm_1_1eEmp.html#dccd02eaa4c45e858a6351723686ac85">00641</a>         <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1eEmp.html#dccd02eaa4c45e858a6351723686ac85" title="Set sample.">set_n</a> ( <span class="keywordtype">int</span> n0, <span class="keywordtype">bool</span> copy=<span class="keyword">true</span> ){<a class="code" href="classbdm_1_1eEmp.html#9d39241aab7c4bbeb07c6d516421c67d" title="Sample weights .">w</a>.set_size(n0,copy);<a class="code" href="classbdm_1_1eEmp.html#73d819553a0f268b055a087d2d4486f3" title="Samples .">samples</a>.set_size(n0,copy);};
-<a name="l00643"></a><a class="code" href="classbdm_1_1eEmp.html#d7f83cc0415cd44ae7cc8b4bdad93aef">00643</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="l00645"></a><a class="code" href="classbdm_1_1eEmp.html#b7d7106f486e3fad38590914a693d714">00645</a>         <span class="keyword">const</span> vec&amp; <a class="code" href="classbdm_1_1eEmp.html#d7f83cc0415cd44ae7cc8b4bdad93aef" title="Potentially dangerous, use with care.">_w</a>()<span class="keyword"> const </span>{<span class="keywordflow">return</span> <a class="code" href="classbdm_1_1eEmp.html#9d39241aab7c4bbeb07c6d516421c67d" title="Sample weights .">w</a>;};
-<a name="l00647"></a><a class="code" href="classbdm_1_1eEmp.html#c24966b0aaeb767bc8a6b4fd60931be2">00647</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="l00649"></a><a class="code" href="classbdm_1_1eEmp.html#b59af0efdb009d98ea8ebfa965e74ae2">00649</a>         <span class="keyword">const</span> Array&lt;vec&gt;&amp; <a class="code" href="classbdm_1_1eEmp.html#c24966b0aaeb767bc8a6b4fd60931be2" title="access function">_samples</a>()<span class="keyword"> const </span>{<span class="keywordflow">return</span> <a class="code" href="classbdm_1_1eEmp.html#73d819553a0f268b055a087d2d4486f3" title="Samples .">samples</a>;};
-<a name="l00651"></a>00651         ivec <a class="code" href="classbdm_1_1eEmp.html#f06ce255de5dbb2313f52ee51f82ba3d" title="Function performs resampling, i.e. removal of low-weight samples and duplication...">resample</a> ( <a class="code" href="namespacebdm.html#33aac0be76ded31d2e3081c5a3f6c418" title="Switch between various resampling methods.">RESAMPLING_METHOD</a> method = SYSTEMATIC );
-<a name="l00653"></a><a class="code" href="classbdm_1_1eEmp.html#97f1e07b5ae6eebc91c7365f0f88d270">00653</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="l00655"></a><a class="code" href="classbdm_1_1eEmp.html#01654c014d3aa068f8d4ecba4be86d09">00655</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="l00656"></a><a class="code" href="classbdm_1_1eEmp.html#bbfcb4f868c7381298c281a256d8c4b9">00656</a>         vec <a class="code" href="classbdm_1_1eEmp.html#bbfcb4f868c7381298c281a256d8c4b9" title="return expected value">mean</a>()<span class="keyword"> const </span>{
-<a name="l00657"></a>00657                 vec pom=zeros ( <a class="code" href="classbdm_1_1epdf.html#62c5b8ff71d9ebe6cd58d3c342eb1dc8" title="Identified of the random variable.">rv</a>.<a class="code" href="classbdm_1_1RV.html#2174751a00ce19f941edd2c1a861be67" title="Return number of scalars in the RV.">count</a>() );
-<a name="l00658"></a>00658                 <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="l00659"></a>00659                 <span class="keywordflow">return</span> pom;
-<a name="l00660"></a>00660         }
-<a name="l00661"></a><a class="code" href="classbdm_1_1eEmp.html#05e9ebf467ede737cb6a3621d7fd3c87">00661</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="l00662"></a>00662                 vec pom=zeros ( <a class="code" href="classbdm_1_1epdf.html#62c5b8ff71d9ebe6cd58d3c342eb1dc8" title="Identified of the random variable.">rv</a>.<a class="code" href="classbdm_1_1RV.html#2174751a00ce19f941edd2c1a861be67" title="Return number of scalars in the RV.">count</a>() );
-<a name="l00663"></a>00663                 <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="l00664"></a>00664                 <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="l00665"></a>00665         }
-<a name="l00666"></a>00666 };
-<a name="l00667"></a>00667 
-<a name="l00668"></a>00668 
-<a name="l00670"></a>00670 
-<a name="l00671"></a>00671 <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt;
-<a name="l00672"></a><a class="code" href="classbdm_1_1enorm.html#7d433390d6bbad337986945b63d7fbe9">00672</a> <a class="code" href="classbdm_1_1enorm.html" title="Gaussian density with positive definite (decomposed) covariance matrix.">enorm&lt;sq_T&gt;::enorm</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;rv ) :<a class="code" href="classbdm_1_1eEF.html" title="General conjugate exponential family posterior density.">eEF</a> ( rv ), mu ( rv.count() ),R ( rv.count() ),dim ( rv.count() ) {};
-<a name="l00673"></a>00673 
-<a name="l00674"></a>00674 <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt;
-<a name="l00675"></a><a class="code" href="classbdm_1_1enorm.html#b8322f2c11560871dd922c660f4771bb">00675</a> <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1enorm.html" title="Gaussian density with positive definite (decomposed) covariance matrix.">enorm&lt;sq_T&gt;::set_parameters</a> ( <span class="keyword">const</span> vec &amp;mu0, <span class="keyword">const</span> sq_T &amp;R0 ) {
-<a name="l00676"></a>00676 <span class="comment">//Fixme test dimensions of mu0 and R0;</span>
-<a name="l00677"></a>00677         <a class="code" href="classbdm_1_1enorm.html#c702a194720853570d08b65482f842c7" title="mean value">mu</a> = mu0;
-<a name="l00678"></a>00678         <a class="code" href="classbdm_1_1enorm.html#2d92dde696b2a7a5b10ddef5d22ba2c2" title="Covariance matrix in decomposed form.">R</a> = R0;
+<a name="l00434"></a>00434 <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt;
+<a name="l00435"></a><a class="code" href="classbdm_1_1mlnorm.html">00435</a> <span class="keyword">class </span><a class="code" href="classbdm_1_1mlnorm.html" title="Normal distributed linear function with linear function of mean value;.">mlnorm</a> : <span class="keyword">public</span> <a class="code" href="classbdm_1_1mEF.html" title="Exponential family model.">mEF</a> {
+<a name="l00436"></a>00436 <span class="keyword">protected</span>:
+<a name="l00438"></a><a class="code" href="classbdm_1_1mlnorm.html#150ad6acb223b0a0abeaf92346686dcd">00438</a>         <a class="code" href="classbdm_1_1enorm.html" title="Gaussian density with positive definite (decomposed) covariance matrix.">enorm&lt;sq_T&gt;</a> <a class="code" href="classbdm_1_1epdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a>;
+<a name="l00439"></a>00439         mat A;
+<a name="l00440"></a>00440         vec mu_const;
+<a name="l00441"></a>00441         vec&amp; _mu; <span class="comment">//cached epdf.mu;</span>
+<a name="l00442"></a>00442 <span class="keyword">public</span>:
+<a name="l00444"></a>00444         <a class="code" href="classbdm_1_1mlnorm.html#2dcc06a3dd71f038efbe14dc34d937ae" title="Constructor.">mlnorm</a> ( );
+<a name="l00446"></a>00446         <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1mlnorm.html#5d18dec3167584338a4775c1d165d96f" title="Set A and R.">set_parameters</a> ( <span class="keyword">const</span>  mat &amp;A, <span class="keyword">const</span> vec &amp;mu0, <span class="keyword">const</span> sq_T &amp;R );
+<a name="l00447"></a>00447 <span class="comment">//      //!Generate one sample of the posterior</span>
+<a name="l00448"></a>00448 <span class="comment">//      vec samplecond (const vec &amp;cond, double &amp;lik );</span>
+<a name="l00449"></a>00449 <span class="comment">//      //!Generate matrix of samples of the posterior</span>
+<a name="l00450"></a>00450 <span class="comment">//      mat samplecond (const vec &amp;cond, vec &amp;lik, int n );</span>
+<a name="l00452"></a>00452 <span class="comment"></span>        <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1mlnorm.html#0dafc0196e7e07fd06dc6716e0e18bc2" title="Set value of rvc . Result of this operation is stored in epdf use function _ep to...">condition</a> ( <span class="keyword">const</span> vec &amp;cond );
+<a name="l00453"></a>00453 
+<a name="l00455"></a><a class="code" href="classbdm_1_1mlnorm.html#56e587952f94fcac6cfc999eae6dbced">00455</a>         vec&amp; <a class="code" href="classbdm_1_1mlnorm.html#56e587952f94fcac6cfc999eae6dbced" title="access function">_mu_const</a>() {<span class="keywordflow">return</span> mu_const;}
+<a name="l00457"></a><a class="code" href="classbdm_1_1mlnorm.html#262a2a486bff585f34bb6a5005b02614">00457</a>         mat&amp; <a class="code" href="classbdm_1_1mlnorm.html#262a2a486bff585f34bb6a5005b02614" title="access function">_A</a>() {<span class="keywordflow">return</span> A;}
+<a name="l00459"></a><a class="code" href="classbdm_1_1mlnorm.html#78120ecd1c2b1d7e80124b4603504604">00459</a>         mat <a class="code" href="classbdm_1_1mlnorm.html#78120ecd1c2b1d7e80124b4603504604" title="access function">_R</a>() {<span class="keywordflow">return</span> <a class="code" href="classbdm_1_1epdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a>._R().to_mat();}
+<a name="l00460"></a>00460 
+<a name="l00461"></a>00461         <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_M&gt;
+<a name="l00462"></a>00462         <span class="keyword">friend</span> std::ostream &amp;operator&lt;&lt; ( std::ostream &amp;os,  mlnorm&lt;sq_M&gt; &amp;ml );
+<a name="l00463"></a>00463 };
+<a name="l00464"></a>00464 
+<a name="l00472"></a><a class="code" href="classbdm_1_1mlstudent.html">00472</a> <span class="keyword">class </span><a class="code" href="classbdm_1_1mlstudent.html">mlstudent</a> : <span class="keyword">public</span> <a class="code" href="classbdm_1_1mlnorm.html" title="Normal distributed linear function with linear function of mean value;.">mlnorm</a>&lt;ldmat&gt; {
+<a name="l00473"></a>00473 <span class="keyword">protected</span>:
+<a name="l00474"></a>00474         <a class="code" href="classldmat.html" title="Matrix stored in LD form, (commonly known as UD).">ldmat</a> Lambda;
+<a name="l00475"></a>00475         <a class="code" href="classldmat.html" title="Matrix stored in LD form, (commonly known as UD).">ldmat</a> &amp;<a class="code" href="classbdm_1_1mlnorm.html#78120ecd1c2b1d7e80124b4603504604" title="access function">_R</a>;
+<a name="l00476"></a>00476         <a class="code" href="classldmat.html" title="Matrix stored in LD form, (commonly known as UD).">ldmat</a> Re;
+<a name="l00477"></a>00477 <span class="keyword">public</span>:
+<a name="l00478"></a>00478         <a class="code" href="classbdm_1_1mlstudent.html">mlstudent</a> ( ) :<a class="code" href="classbdm_1_1mlnorm.html" title="Normal distributed linear function with linear function of mean value;.">mlnorm&lt;ldmat&gt;</a> (),
+<a name="l00479"></a>00479                         Lambda (),      <a class="code" href="classbdm_1_1mlnorm.html#78120ecd1c2b1d7e80124b4603504604" title="access function">_R</a> ( <a class="code" href="classbdm_1_1epdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a>._R() ) {}
+<a name="l00480"></a>00480         <span class="keywordtype">void</span> set_parameters ( <span class="keyword">const</span> mat &amp;A0, <span class="keyword">const</span> vec &amp;mu0, <span class="keyword">const</span> <a class="code" href="classldmat.html" title="Matrix stored in LD form, (commonly known as UD).">ldmat</a> &amp;R0, <span class="keyword">const</span> <a class="code" href="classldmat.html" title="Matrix stored in LD form, (commonly known as UD).">ldmat</a>&amp; Lambda0 ) {
+<a name="l00481"></a>00481                 it_assert_debug ( A0.rows() ==mu0.length(),<span class="stringliteral">""</span> );
+<a name="l00482"></a>00482                 it_assert_debug ( R0.<a class="code" href="group__math.html#g96dfb21865db4f5bd36fa70f9b0b1163" title="access function">rows</a>() ==A0.rows(),<span class="stringliteral">""</span> );
+<a name="l00483"></a>00483 
+<a name="l00484"></a>00484                 <a class="code" href="classbdm_1_1epdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a>.set_parameters ( mu0,Lambda ); <span class="comment">//</span>
+<a name="l00485"></a>00485                 A = A0;
+<a name="l00486"></a>00486                 mu_const = mu0;
+<a name="l00487"></a>00487                 Re=R0;
+<a name="l00488"></a>00488                 Lambda = Lambda0;
+<a name="l00489"></a>00489         }
+<a name="l00490"></a><a class="code" href="classbdm_1_1mlstudent.html#efd37560585c8613897f30d3c2f58d0d">00490</a>         <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1mlstudent.html#efd37560585c8613897f30d3c2f58d0d" title="Set value of rvc . Result of this operation is stored in epdf use function _ep to...">condition</a> ( <span class="keyword">const</span> vec &amp;cond ) {
+<a name="l00491"></a>00491                 _mu = A*cond + mu_const;
+<a name="l00492"></a>00492                 <span class="keywordtype">double</span> zeta;
+<a name="l00493"></a>00493                 <span class="comment">//ugly hack!</span>
+<a name="l00494"></a>00494                 <span class="keywordflow">if</span> ( ( cond.length() +1 ) ==Lambda.<a class="code" href="group__math.html#g96dfb21865db4f5bd36fa70f9b0b1163" title="access function">rows</a>() ) {
+<a name="l00495"></a>00495                         zeta = Lambda.<a class="code" href="classldmat.html#d876c5f83e02b3e809b35c9de5068f14" title="Evaluates quadratic form ;.">invqform</a> ( <a class="code" href="namespacebdm.html#b9016687c0e874ca5cdcf75ae28811aa" title="Concat two random variables.">concat</a> ( cond, vec_1 ( 1.0 ) ) );
+<a name="l00496"></a>00496                 }
+<a name="l00497"></a>00497                 <span class="keywordflow">else</span> {
+<a name="l00498"></a>00498                         zeta = Lambda.<a class="code" href="classldmat.html#d876c5f83e02b3e809b35c9de5068f14" title="Evaluates quadratic form ;.">invqform</a> ( cond );
+<a name="l00499"></a>00499                 }
+<a name="l00500"></a>00500                 <a class="code" href="classbdm_1_1mlnorm.html#78120ecd1c2b1d7e80124b4603504604" title="access function">_R</a> = Re;
+<a name="l00501"></a>00501                 <a class="code" href="classbdm_1_1mlnorm.html#78120ecd1c2b1d7e80124b4603504604" title="access function">_R</a>*= ( 1+zeta );<span class="comment">// / ( nu ); &lt;&lt; nu is in Re!!!!!!</span>
+<a name="l00502"></a>00502         };
+<a name="l00503"></a>00503 
+<a name="l00504"></a>00504 };
+<a name="l00514"></a><a class="code" href="classbdm_1_1mgamma.html">00514</a> <span class="keyword">class </span><a class="code" href="classbdm_1_1mgamma.html" title="Gamma random walk.">mgamma</a> : <span class="keyword">public</span> <a class="code" href="classbdm_1_1mEF.html" title="Exponential family model.">mEF</a> {
+<a name="l00515"></a>00515 <span class="keyword">protected</span>:
+<a name="l00517"></a><a class="code" href="classbdm_1_1mgamma.html#bdc9f1e9e03c09e91103fee269864438">00517</a>         <a class="code" href="classbdm_1_1egamma.html" title="Gamma posterior density.">egamma</a> <a class="code" href="classbdm_1_1epdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a>;
+<a name="l00519"></a><a class="code" href="classbdm_1_1mgamma.html#b20cf88cca1fe9b0b8f2a412608bfd09">00519</a>         <span class="keywordtype">double</span> <a class="code" href="classbdm_1_1mgamma.html#b20cf88cca1fe9b0b8f2a412608bfd09" title="Constant .">k</a>;
+<a name="l00521"></a><a class="code" href="classbdm_1_1mgamma.html#3d95f4dde9214ff6dba265e18af60312">00521</a>         vec &amp;<a class="code" href="classbdm_1_1mgamma.html#3d95f4dde9214ff6dba265e18af60312" title="cache of epdf.beta">_beta</a>;
+<a name="l00522"></a>00522 
+<a name="l00523"></a>00523 <span class="keyword">public</span>:
+<a name="l00525"></a><a class="code" href="classbdm_1_1mgamma.html#1a9dc8661e5b214a8185d6e6b9956eb1">00525</a>         <a class="code" href="classbdm_1_1mgamma.html#1a9dc8661e5b214a8185d6e6b9956eb1" title="Constructor.">mgamma</a> ( ) : <a class="code" href="classbdm_1_1mEF.html" title="Exponential family model.">mEF</a> ( ), <a class="code" href="classbdm_1_1epdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a> (), <a class="code" href="classbdm_1_1mgamma.html#3d95f4dde9214ff6dba265e18af60312" title="cache of epdf.beta">_beta</a> ( <a class="code" href="classbdm_1_1epdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a>.<a class="code" href="classbdm_1_1mgamma.html#3d95f4dde9214ff6dba265e18af60312" title="cache of epdf.beta">_beta</a>() ) {<a class="code" href="classbdm_1_1mpdf.html#5eea43c56d38e4441bfb30270db949c0" title="pointer to internal epdf">ep</a>=&amp;<a class="code" href="classbdm_1_1epdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a>;};
+<a name="l00527"></a>00527         <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1mgamma.html#0b486f7e52a3d8a39adbcbd325461c0d" title="Set value of k.">set_parameters</a> ( <span class="keywordtype">double</span> <a class="code" href="classbdm_1_1mgamma.html#b20cf88cca1fe9b0b8f2a412608bfd09" title="Constant .">k</a> );
+<a name="l00528"></a><a class="code" href="classbdm_1_1mgamma.html#8996500f1885e39cde30221b20900bff">00528</a>         <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1mgamma.html#8996500f1885e39cde30221b20900bff" title="Update ep so that it represents this mpdf conditioned on rvc = cond.">condition</a> ( <span class="keyword">const</span> vec &amp;val ) {<a class="code" href="classbdm_1_1mgamma.html#3d95f4dde9214ff6dba265e18af60312" title="cache of epdf.beta">_beta</a>=<a class="code" href="classbdm_1_1mgamma.html#b20cf88cca1fe9b0b8f2a412608bfd09" title="Constant .">k</a>/val;};
+<a name="l00529"></a>00529 };
+<a name="l00530"></a>00530 
+<a name="l00540"></a><a class="code" href="classbdm_1_1migamma.html">00540</a> <span class="keyword">class </span><a class="code" href="classbdm_1_1migamma.html" title="Inverse-Gamma random walk.">migamma</a> : <span class="keyword">public</span> <a class="code" href="classbdm_1_1mEF.html" title="Exponential family model.">mEF</a> {
+<a name="l00541"></a>00541 <span class="keyword">protected</span>:
+<a name="l00543"></a><a class="code" href="classbdm_1_1migamma.html#a31b39d4179551b593c9e0d7d756783a">00543</a>         <a class="code" href="classbdm_1_1eigamma.html" title="Inverse-Gamma posterior density.">eigamma</a> <a class="code" href="classbdm_1_1epdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a>;
+<a name="l00545"></a><a class="code" href="classbdm_1_1migamma.html#dc56bc9da542e0103ec16b9be8e5e38c">00545</a>         <span class="keywordtype">double</span> <a class="code" href="classbdm_1_1migamma.html#dc56bc9da542e0103ec16b9be8e5e38c" title="Constant .">k</a>;
+<a name="l00547"></a><a class="code" href="classbdm_1_1migamma.html#c9847093da59a9ba0ebb68d2c592f5dc">00547</a>         vec &amp;<a class="code" href="classbdm_1_1migamma.html#c9847093da59a9ba0ebb68d2c592f5dc" title="cache of epdf.alpha">_alpha</a>;
+<a name="l00549"></a><a class="code" href="classbdm_1_1migamma.html#0d854c047001b5465cf1ba21f52904b5">00549</a>         vec &amp;<a class="code" href="classbdm_1_1migamma.html#0d854c047001b5465cf1ba21f52904b5" title="cache of epdf.beta">_beta</a>;
+<a name="l00550"></a>00550 
+<a name="l00551"></a>00551 <span class="keyword">public</span>:
+<a name="l00554"></a>00554         <a class="code" href="classbdm_1_1migamma.html" title="Inverse-Gamma random walk.">migamma</a> ( ) : <a class="code" href="classbdm_1_1mEF.html" title="Exponential family model.">mEF</a> (), <a class="code" href="classbdm_1_1epdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a> ( ), <a class="code" href="classbdm_1_1migamma.html#c9847093da59a9ba0ebb68d2c592f5dc" title="cache of epdf.alpha">_alpha</a> ( <a class="code" href="classbdm_1_1epdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a>.<a class="code" href="classbdm_1_1migamma.html#c9847093da59a9ba0ebb68d2c592f5dc" title="cache of epdf.alpha">_alpha</a>() ), <a class="code" href="classbdm_1_1migamma.html#0d854c047001b5465cf1ba21f52904b5" title="cache of epdf.beta">_beta</a> ( <a class="code" href="classbdm_1_1epdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a>.<a class="code" href="classbdm_1_1migamma.html#0d854c047001b5465cf1ba21f52904b5" title="cache of epdf.beta">_beta</a>() ) {<a class="code" href="classbdm_1_1mpdf.html#5eea43c56d38e4441bfb30270db949c0" title="pointer to internal epdf">ep</a>=&amp;<a class="code" href="classbdm_1_1epdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a>;};
+<a name="l00555"></a>00555         <a class="code" href="classbdm_1_1migamma.html" title="Inverse-Gamma random walk.">migamma</a> ( <span class="keyword">const</span> <a class="code" href="classbdm_1_1migamma.html" title="Inverse-Gamma random walk.">migamma</a> &amp;m ) : <a class="code" href="classbdm_1_1mEF.html" title="Exponential family model.">mEF</a> (), <a class="code" href="classbdm_1_1epdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a> ( m.<a class="code" href="classbdm_1_1epdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a> ), <a class="code" href="classbdm_1_1migamma.html#c9847093da59a9ba0ebb68d2c592f5dc" title="cache of epdf.alpha">_alpha</a> ( <a class="code" href="classbdm_1_1epdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a>.<a class="code" href="classbdm_1_1migamma.html#c9847093da59a9ba0ebb68d2c592f5dc" title="cache of epdf.alpha">_alpha</a>() ), <a class="code" href="classbdm_1_1migamma.html#0d854c047001b5465cf1ba21f52904b5" title="cache of epdf.beta">_beta</a> ( <a class="code" href="classbdm_1_1epdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a>.<a class="code" href="classbdm_1_1migamma.html#0d854c047001b5465cf1ba21f52904b5" title="cache of epdf.beta">_beta</a>() ) {<a class="code" href="classbdm_1_1mpdf.html#5eea43c56d38e4441bfb30270db949c0" title="pointer to internal epdf">ep</a>=&amp;<a class="code" href="classbdm_1_1epdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a>;};
+<a name="l00557"></a>00557 
+<a name="l00559"></a><a class="code" href="classbdm_1_1migamma.html#8b10ab922e2a7bae2fb6bb3efc7b6151">00559</a>         <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1migamma.html#8b10ab922e2a7bae2fb6bb3efc7b6151" title="Set value of k.">set_parameters</a> ( <span class="keywordtype">int</span> len, <span class="keywordtype">double</span> k0 ) {
+<a name="l00560"></a>00560                 <a class="code" href="classbdm_1_1migamma.html#dc56bc9da542e0103ec16b9be8e5e38c" title="Constant .">k</a>=k0;
+<a name="l00561"></a>00561                 <a class="code" href="classbdm_1_1epdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a>.set_parameters ( ( 1.0/ ( <a class="code" href="classbdm_1_1migamma.html#dc56bc9da542e0103ec16b9be8e5e38c" title="Constant .">k</a>*<a class="code" href="classbdm_1_1migamma.html#dc56bc9da542e0103ec16b9be8e5e38c" title="Constant .">k</a> ) +2.0 ) *ones ( len ) <span class="comment">/*alpha*/</span>, ones ( len ) <span class="comment">/*beta*/</span> );
+<a name="l00562"></a>00562         };
+<a name="l00563"></a><a class="code" href="classbdm_1_1migamma.html#7a34b1e2e3aa2250d7c0ed7df1665b8c">00563</a>         <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1migamma.html#7a34b1e2e3aa2250d7c0ed7df1665b8c" title="Update ep so that it represents this mpdf conditioned on rvc = cond.">condition</a> ( <span class="keyword">const</span> vec &amp;val ) {
+<a name="l00564"></a>00564                 <a class="code" href="classbdm_1_1migamma.html#0d854c047001b5465cf1ba21f52904b5" title="cache of epdf.beta">_beta</a>=elem_mult ( val, ( <a class="code" href="classbdm_1_1migamma.html#c9847093da59a9ba0ebb68d2c592f5dc" title="cache of epdf.alpha">_alpha</a>-1.0 ) );
+<a name="l00565"></a>00565         };
+<a name="l00566"></a>00566 };
+<a name="l00567"></a>00567 
+<a name="l00579"></a><a class="code" href="classbdm_1_1mgamma__fix.html">00579</a> <span class="keyword">class </span><a class="code" href="classbdm_1_1mgamma__fix.html" title="Gamma random walk around a fixed point.">mgamma_fix</a> : <span class="keyword">public</span> <a class="code" href="classbdm_1_1mgamma.html" title="Gamma random walk.">mgamma</a> {
+<a name="l00580"></a>00580 <span class="keyword">protected</span>:
+<a name="l00582"></a><a class="code" href="classbdm_1_1mgamma__fix.html#1eb701506aabb2e6af007e487212d6fa">00582</a>         <span class="keywordtype">double</span> <a class="code" href="classbdm_1_1mgamma__fix.html#1eb701506aabb2e6af007e487212d6fa" title="parameter l">l</a>;
+<a name="l00584"></a><a class="code" href="classbdm_1_1mgamma__fix.html#018c6f901a04e419455308a07eb3b0b2">00584</a>         vec <a class="code" href="classbdm_1_1mgamma__fix.html#018c6f901a04e419455308a07eb3b0b2" title="reference vector">refl</a>;
+<a name="l00585"></a>00585 <span class="keyword">public</span>:
+<a name="l00587"></a><a class="code" href="classbdm_1_1mgamma__fix.html#9a31bc9b4b60188a18a2a6b588dc4b2d">00587</a>         <a class="code" href="classbdm_1_1mgamma__fix.html#9a31bc9b4b60188a18a2a6b588dc4b2d" title="Constructor.">mgamma_fix</a> ( ) : <a class="code" href="classbdm_1_1mgamma.html" title="Gamma random walk.">mgamma</a> ( ),<a class="code" href="classbdm_1_1mgamma__fix.html#018c6f901a04e419455308a07eb3b0b2" title="reference vector">refl</a> () {};
+<a name="l00589"></a><a class="code" href="classbdm_1_1mgamma__fix.html#1bfd30e90db9dc1fbda4a9fbb0b716b2">00589</a>         <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1mgamma__fix.html#1bfd30e90db9dc1fbda4a9fbb0b716b2" title="Set value of k.">set_parameters</a> ( <span class="keywordtype">double</span> k0 , vec ref0, <span class="keywordtype">double</span> l0 ) {
+<a name="l00590"></a>00590                 <a class="code" href="classbdm_1_1mgamma.html#0b486f7e52a3d8a39adbcbd325461c0d" title="Set value of k.">mgamma::set_parameters</a> ( k0 );
+<a name="l00591"></a>00591                 <a class="code" href="classbdm_1_1mgamma__fix.html#018c6f901a04e419455308a07eb3b0b2" title="reference vector">refl</a>=pow ( ref0,1.0-l0 );<a class="code" href="classbdm_1_1mgamma__fix.html#1eb701506aabb2e6af007e487212d6fa" title="parameter l">l</a>=l0;
+<a name="l00592"></a>00592         };
+<a name="l00593"></a>00593 
+<a name="l00594"></a><a class="code" href="classbdm_1_1mgamma__fix.html#1d539591deb7a38bb3403c2b396c8ff7">00594</a>         <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1mgamma__fix.html#1d539591deb7a38bb3403c2b396c8ff7" title="Update ep so that it represents this mpdf conditioned on rvc = cond.">condition</a> ( <span class="keyword">const</span> vec &amp;val ) {vec mean=elem_mult ( <a class="code" href="classbdm_1_1mgamma__fix.html#018c6f901a04e419455308a07eb3b0b2" title="reference vector">refl</a>,pow ( val,<a class="code" href="classbdm_1_1mgamma__fix.html#1eb701506aabb2e6af007e487212d6fa" title="parameter l">l</a> ) ); <a class="code" href="classbdm_1_1mgamma.html#3d95f4dde9214ff6dba265e18af60312" title="cache of epdf.beta">_beta</a>=<a class="code" href="classbdm_1_1mgamma.html#b20cf88cca1fe9b0b8f2a412608bfd09" title="Constant .">k</a>/mean;};
+<a name="l00595"></a>00595 };
+<a name="l00596"></a>00596 
+<a name="l00597"></a>00597 
+<a name="l00610"></a><a class="code" href="classbdm_1_1migamma__fix.html">00610</a> <span class="keyword">class </span><a class="code" href="classbdm_1_1migamma__fix.html" title="Inverse-Gamma random walk around a fixed point.">migamma_fix</a> : <span class="keyword">public</span> <a class="code" href="classbdm_1_1migamma.html" title="Inverse-Gamma random walk.">migamma</a> {
+<a name="l00611"></a>00611 <span class="keyword">protected</span>:
+<a name="l00613"></a><a class="code" href="classbdm_1_1migamma__fix.html#e1c344accac36d7ccc3ffa502e8d2f4e">00613</a>         <span class="keywordtype">double</span> <a class="code" href="classbdm_1_1migamma__fix.html#e1c344accac36d7ccc3ffa502e8d2f4e" title="parameter l">l</a>;
+<a name="l00615"></a><a class="code" href="classbdm_1_1migamma__fix.html#5d453e5a2bdfc9a16c8acb8842dc9780">00615</a>         vec <a class="code" href="classbdm_1_1migamma__fix.html#5d453e5a2bdfc9a16c8acb8842dc9780" title="reference vector">refl</a>;
+<a name="l00616"></a>00616 <span class="keyword">public</span>:
+<a name="l00618"></a><a class="code" href="classbdm_1_1migamma__fix.html#42a61f9468b2c435386f47ae8a5ddf7e">00618</a>         <a class="code" href="classbdm_1_1migamma__fix.html#42a61f9468b2c435386f47ae8a5ddf7e" title="Constructor.">migamma_fix</a> ( ) : <a class="code" href="classbdm_1_1migamma.html" title="Inverse-Gamma random walk.">migamma</a> (),<a class="code" href="classbdm_1_1migamma__fix.html#5d453e5a2bdfc9a16c8acb8842dc9780" title="reference vector">refl</a> ( ) {};
+<a name="l00620"></a><a class="code" href="classbdm_1_1migamma__fix.html#17f9ce1068660a4e8c05173bef7d7440">00620</a>         <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1migamma__fix.html#17f9ce1068660a4e8c05173bef7d7440" title="Set value of k.">set_parameters</a> ( <span class="keywordtype">double</span> k0 , vec ref0, <span class="keywordtype">double</span> l0 ) {
+<a name="l00621"></a>00621                 <a class="code" href="classbdm_1_1migamma.html#8b10ab922e2a7bae2fb6bb3efc7b6151" title="Set value of k.">migamma::set_parameters</a> (ref0.length(), k0 );
+<a name="l00622"></a>00622                 <a class="code" href="classbdm_1_1migamma__fix.html#5d453e5a2bdfc9a16c8acb8842dc9780" title="reference vector">refl</a>=pow ( ref0,1.0-l0 );
+<a name="l00623"></a>00623                 <a class="code" href="classbdm_1_1migamma__fix.html#e1c344accac36d7ccc3ffa502e8d2f4e" title="parameter l">l</a>=l0;
+<a name="l00624"></a>00624         };
+<a name="l00625"></a>00625 
+<a name="l00626"></a><a class="code" href="classbdm_1_1migamma__fix.html#cfbabd293795d44aae1b7c874e57c4b8">00626</a>         <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1migamma__fix.html#cfbabd293795d44aae1b7c874e57c4b8" title="Update ep so that it represents this mpdf conditioned on rvc = cond.">condition</a> ( <span class="keyword">const</span> vec &amp;val ) {
+<a name="l00627"></a>00627                 vec mean=elem_mult ( <a class="code" href="classbdm_1_1migamma__fix.html#5d453e5a2bdfc9a16c8acb8842dc9780" title="reference vector">refl</a>,pow ( val,<a class="code" href="classbdm_1_1migamma__fix.html#e1c344accac36d7ccc3ffa502e8d2f4e" title="parameter l">l</a> ) );
+<a name="l00628"></a>00628                 <a class="code" href="classbdm_1_1migamma.html#7a34b1e2e3aa2250d7c0ed7df1665b8c" title="Update ep so that it represents this mpdf conditioned on rvc = cond.">migamma::condition</a> ( mean );
+<a name="l00629"></a>00629         };
+<a name="l00630"></a>00630 };
+<a name="l00632"></a><a class="code" href="namespacebdm.html#33aac0be76ded31d2e3081c5a3f6c418">00632</a> <span class="keyword">enum</span> <a class="code" href="namespacebdm.html#33aac0be76ded31d2e3081c5a3f6c418" title="Switch between various resampling methods.">RESAMPLING_METHOD</a> { MULTINOMIAL = 0, STRATIFIED = 1, SYSTEMATIC = 3 };
+<a name="l00638"></a><a class="code" href="classbdm_1_1eEmp.html">00638</a> <span class="keyword">class </span><a class="code" href="classbdm_1_1eEmp.html" title="Weighted empirical density.">eEmp</a>: <span class="keyword">public</span> <a class="code" href="classbdm_1_1epdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a> {
+<a name="l00639"></a>00639 <span class="keyword">protected</span> :
+<a name="l00641"></a><a class="code" href="classbdm_1_1eEmp.html#9798006271ca77629855113f1283a031">00641</a>         <span class="keywordtype">int</span> <a class="code" href="classbdm_1_1eEmp.html#9798006271ca77629855113f1283a031" title="Number of particles.">n</a>;
+<a name="l00643"></a><a class="code" href="classbdm_1_1eEmp.html#9d39241aab7c4bbeb07c6d516421c67d">00643</a>         vec <a class="code" href="classbdm_1_1eEmp.html#9d39241aab7c4bbeb07c6d516421c67d" title="Sample weights .">w</a>;
+<a name="l00645"></a><a class="code" href="classbdm_1_1eEmp.html#73d819553a0f268b055a087d2d4486f3">00645</a>         Array&lt;vec&gt; <a class="code" href="classbdm_1_1eEmp.html#73d819553a0f268b055a087d2d4486f3" title="Samples .">samples</a>;
+<a name="l00646"></a>00646 <span class="keyword">public</span>:
+<a name="l00648"></a><a class="code" href="classbdm_1_1eEmp.html#34d7f929fbab6c1e4ce318c56a6399c7">00648</a>         <a class="code" href="classbdm_1_1eEmp.html#34d7f929fbab6c1e4ce318c56a6399c7" title="Default constructor.">eEmp</a> ( <span class="keywordtype">int</span> n0 ) :<a class="code" href="classbdm_1_1epdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a> ( ),<a class="code" href="classbdm_1_1eEmp.html#9798006271ca77629855113f1283a031" title="Number of particles.">n</a> ( n0 ),<a class="code" href="classbdm_1_1eEmp.html#9d39241aab7c4bbeb07c6d516421c67d" title="Sample weights .">w</a> ( <a class="code" href="classbdm_1_1eEmp.html#9798006271ca77629855113f1283a031" title="Number of particles.">n</a> ),<a class="code" href="classbdm_1_1eEmp.html#73d819553a0f268b055a087d2d4486f3" title="Samples .">samples</a> ( <a class="code" href="classbdm_1_1eEmp.html#9798006271ca77629855113f1283a031" title="Number of particles.">n</a> ) {};
+<a name="l00650"></a>00650         <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1eEmp.html#82320074a9b0ad7e1bb33a6e885b65d7" title="Set samples and weights.">set_parameters</a> ( <span class="keyword">const</span> vec &amp;w0, <span class="keyword">const</span> <a class="code" href="classbdm_1_1epdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a>* pdf0 );
+<a name="l00652"></a>00652         <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1eEmp.html#b62d802b8ef39f7c4dcbeb366c90951a" title="Set sample.">set_samples</a> ( <span class="keyword">const</span> <a class="code" href="classbdm_1_1epdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a>* pdf0 );
+<a name="l00654"></a><a class="code" href="classbdm_1_1eEmp.html#dccd02eaa4c45e858a6351723686ac85">00654</a>         <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1eEmp.html#dccd02eaa4c45e858a6351723686ac85" title="Set sample.">set_n</a> ( <span class="keywordtype">int</span> n0, <span class="keywordtype">bool</span> copy=<span class="keyword">true</span> ) {<a class="code" href="classbdm_1_1eEmp.html#9d39241aab7c4bbeb07c6d516421c67d" title="Sample weights .">w</a>.set_size ( n0,copy );<a class="code" href="classbdm_1_1eEmp.html#73d819553a0f268b055a087d2d4486f3" title="Samples .">samples</a>.set_size ( n0,copy );};
+<a name="l00656"></a><a class="code" href="classbdm_1_1eEmp.html#d7f83cc0415cd44ae7cc8b4bdad93aef">00656</a>         vec&amp; <a class="code" href="classbdm_1_1eEmp.html#d7f83cc0415cd44ae7cc8b4bdad93aef" title="Potentially dangerous, use with care.">_w</a>()  {<span class="keywordflow">return</span> <a class="code" href="classbdm_1_1eEmp.html#9d39241aab7c4bbeb07c6d516421c67d" title="Sample weights .">w</a>;};
+<a name="l00658"></a><a class="code" href="classbdm_1_1eEmp.html#b7d7106f486e3fad38590914a693d714">00658</a>         <span class="keyword">const</span> vec&amp; <a class="code" href="classbdm_1_1eEmp.html#d7f83cc0415cd44ae7cc8b4bdad93aef" title="Potentially dangerous, use with care.">_w</a>()<span class="keyword"> const </span>{<span class="keywordflow">return</span> <a class="code" href="classbdm_1_1eEmp.html#9d39241aab7c4bbeb07c6d516421c67d" title="Sample weights .">w</a>;};
+<a name="l00660"></a><a class="code" href="classbdm_1_1eEmp.html#c24966b0aaeb767bc8a6b4fd60931be2">00660</a>         Array&lt;vec&gt;&amp; <a class="code" href="classbdm_1_1eEmp.html#c24966b0aaeb767bc8a6b4fd60931be2" title="access function">_samples</a>() {<span class="keywordflow">return</span> <a class="code" href="classbdm_1_1eEmp.html#73d819553a0f268b055a087d2d4486f3" title="Samples .">samples</a>;};
+<a name="l00662"></a><a class="code" href="classbdm_1_1eEmp.html#b59af0efdb009d98ea8ebfa965e74ae2">00662</a>         <span class="keyword">const</span> Array&lt;vec&gt;&amp; <a class="code" href="classbdm_1_1eEmp.html#c24966b0aaeb767bc8a6b4fd60931be2" title="access function">_samples</a>()<span class="keyword"> const </span>{<span class="keywordflow">return</span> <a class="code" href="classbdm_1_1eEmp.html#73d819553a0f268b055a087d2d4486f3" title="Samples .">samples</a>;};
+<a name="l00664"></a>00664         ivec <a class="code" href="classbdm_1_1eEmp.html#f06ce255de5dbb2313f52ee51f82ba3d" title="Function performs resampling, i.e. removal of low-weight samples and duplication...">resample</a> ( <a class="code" href="namespacebdm.html#33aac0be76ded31d2e3081c5a3f6c418" title="Switch between various resampling methods.">RESAMPLING_METHOD</a> method = SYSTEMATIC );
+<a name="l00666"></a><a class="code" href="classbdm_1_1eEmp.html#97f1e07b5ae6eebc91c7365f0f88d270">00666</a>         vec <a class="code" href="classbdm_1_1eEmp.html#97f1e07b5ae6eebc91c7365f0f88d270" title="inherited operation : NOT implemneted">sample</a>()<span class="keyword"> const </span>{it_error ( <span class="stringliteral">"Not implemented"</span> );<span class="keywordflow">return</span> 0;}
+<a name="l00668"></a><a class="code" href="classbdm_1_1eEmp.html#01654c014d3aa068f8d4ecba4be86d09">00668</a>         <span class="keywordtype">double</span> <a class="code" href="classbdm_1_1eEmp.html#01654c014d3aa068f8d4ecba4be86d09" title="inherited operation : NOT implemneted">evallog</a> ( <span class="keyword">const</span> vec &amp;val )<span class="keyword"> const </span>{it_error ( <span class="stringliteral">"Not implemented"</span> );<span class="keywordflow">return</span> 0.0;}
+<a name="l00669"></a><a class="code" href="classbdm_1_1eEmp.html#bbfcb4f868c7381298c281a256d8c4b9">00669</a>         vec <a class="code" href="classbdm_1_1eEmp.html#bbfcb4f868c7381298c281a256d8c4b9" title="return expected value">mean</a>()<span class="keyword"> const </span>{
+<a name="l00670"></a>00670                 vec pom=zeros ( <a class="code" href="classbdm_1_1epdf.html#16adac20ec7fe07e1ea0b27d917788ce" title="dimension of the random variable">dim</a> );
+<a name="l00671"></a>00671                 <span class="keywordflow">for</span> ( <span class="keywordtype">int</span> i=0;i&lt;<a class="code" href="classbdm_1_1eEmp.html#9798006271ca77629855113f1283a031" title="Number of particles.">n</a>;i++ ) {pom+=<a class="code" href="classbdm_1_1eEmp.html#73d819553a0f268b055a087d2d4486f3" title="Samples .">samples</a> ( i ) *<a class="code" href="classbdm_1_1eEmp.html#9d39241aab7c4bbeb07c6d516421c67d" title="Sample weights .">w</a> ( i );}
+<a name="l00672"></a>00672                 <span class="keywordflow">return</span> pom;
+<a name="l00673"></a>00673         }
+<a name="l00674"></a><a class="code" href="classbdm_1_1eEmp.html#05e9ebf467ede737cb6a3621d7fd3c87">00674</a>         vec <a class="code" href="classbdm_1_1eEmp.html#05e9ebf467ede737cb6a3621d7fd3c87" title="return expected variance (not covariance!)">variance</a>()<span class="keyword"> const </span>{
+<a name="l00675"></a>00675                 vec pom=zeros ( <a class="code" href="classbdm_1_1epdf.html#16adac20ec7fe07e1ea0b27d917788ce" title="dimension of the random variable">dim</a> );
+<a name="l00676"></a>00676                 <span class="keywordflow">for</span> ( <span class="keywordtype">int</span> i=0;i&lt;<a class="code" href="classbdm_1_1eEmp.html#9798006271ca77629855113f1283a031" title="Number of particles.">n</a>;i++ ) {pom+=pow ( <a class="code" href="classbdm_1_1eEmp.html#73d819553a0f268b055a087d2d4486f3" title="Samples .">samples</a> ( i ),2 ) *<a class="code" href="classbdm_1_1eEmp.html#9d39241aab7c4bbeb07c6d516421c67d" title="Sample weights .">w</a> ( i );}
+<a name="l00677"></a>00677                 <span class="keywordflow">return</span> pom-pow ( <a class="code" href="classbdm_1_1eEmp.html#bbfcb4f868c7381298c281a256d8c4b9" title="return expected value">mean</a>(),2 );
+<a name="l00678"></a>00678         }
 <a name="l00679"></a>00679 };
 <a name="l00680"></a>00680 
-<a name="l00681"></a>00681 <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt;
-<a name="l00682"></a><a class="code" href="classbdm_1_1enorm.html#d2e0d3a1e30ab3ab04df2d0c43ae74a2">00682</a> <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1enorm.html" title="Gaussian density with positive definite (decomposed) covariance matrix.">enorm&lt;sq_T&gt;::dupdate</a> ( mat &amp;v, <span class="keywordtype">double</span> nu ) {
-<a name="l00683"></a>00683         <span class="comment">//</span>
-<a name="l00684"></a>00684 };
-<a name="l00685"></a>00685 
-<a name="l00686"></a>00686 <span class="comment">// template&lt;class sq_T&gt;</span>
-<a name="l00687"></a>00687 <span class="comment">// void enorm&lt;sq_T&gt;::tupdate ( double phi, mat &amp;vbar, double nubar ) {</span>
-<a name="l00688"></a>00688 <span class="comment">//      //</span>
-<a name="l00689"></a>00689 <span class="comment">// };</span>
-<a name="l00690"></a>00690 
-<a name="l00691"></a>00691 <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt;
-<a name="l00692"></a><a class="code" href="classbdm_1_1enorm.html#e1a48f52351ec3a349bd443b713b1766">00692</a> vec <a class="code" href="classbdm_1_1enorm.html" title="Gaussian density with positive definite (decomposed) covariance matrix.">enorm&lt;sq_T&gt;::sample</a>()<span class="keyword"> const </span>{
-<a name="l00693"></a>00693         vec x ( <a class="code" href="classbdm_1_1enorm.html#91a2d4a91364b0144e1523cad4d1135b" title="dimension (redundant from rv.count() for easier coding )">dim</a> );
-<a name="l00694"></a>00694 <span class="preprocessor">        #pragma omp critical </span>
-<a name="l00695"></a>00695 <span class="preprocessor"></span>        <a class="code" href="namespacebdm.html#c959a7382efbcc31af4b58cf0f0f951a" title="Global Normal_RNG.">NorRNG</a>.sample_vector ( <a class="code" href="classbdm_1_1enorm.html#91a2d4a91364b0144e1523cad4d1135b" title="dimension (redundant from rv.count() for easier coding )">dim</a>,x );
-<a name="l00696"></a>00696         vec smp = <a class="code" href="classbdm_1_1enorm.html#2d92dde696b2a7a5b10ddef5d22ba2c2" title="Covariance matrix in decomposed form.">R</a>.sqrt_mult ( x );
-<a name="l00697"></a>00697 
-<a name="l00698"></a>00698         smp += <a class="code" href="classbdm_1_1enorm.html#c702a194720853570d08b65482f842c7" title="mean value">mu</a>;
-<a name="l00699"></a>00699         <span class="keywordflow">return</span> smp;
-<a name="l00700"></a>00700 };
-<a name="l00701"></a>00701 
-<a name="l00702"></a>00702 <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt;
-<a name="l00703"></a><a class="code" href="classbdm_1_1enorm.html#ebd96125aed74f9504033bb3605849db">00703</a> mat <a class="code" href="classbdm_1_1enorm.html" title="Gaussian density with positive definite (decomposed) covariance matrix.">enorm&lt;sq_T&gt;::sample</a> ( <span class="keywordtype">int</span> N )<span class="keyword"> const </span>{
-<a name="l00704"></a>00704         mat X ( <a class="code" href="classbdm_1_1enorm.html#91a2d4a91364b0144e1523cad4d1135b" title="dimension (redundant from rv.count() for easier coding )">dim</a>,N );
-<a name="l00705"></a>00705         vec x ( <a class="code" href="classbdm_1_1enorm.html#91a2d4a91364b0144e1523cad4d1135b" title="dimension (redundant from rv.count() for easier coding )">dim</a> );
-<a name="l00706"></a>00706         vec pom;
-<a name="l00707"></a>00707         <span class="keywordtype">int</span> i;
-<a name="l00708"></a>00708 
-<a name="l00709"></a>00709         <span class="keywordflow">for</span> ( i=0;i&lt;N;i++ ) {
-<a name="l00710"></a>00710 <span class="preprocessor">        #pragma omp critical </span>
-<a name="l00711"></a>00711 <span class="preprocessor"></span>                <a class="code" href="namespacebdm.html#c959a7382efbcc31af4b58cf0f0f951a" title="Global Normal_RNG.">NorRNG</a>.sample_vector ( <a class="code" href="classbdm_1_1enorm.html#91a2d4a91364b0144e1523cad4d1135b" title="dimension (redundant from rv.count() for easier coding )">dim</a>,x );
-<a name="l00712"></a>00712                 pom = <a class="code" href="classbdm_1_1enorm.html#2d92dde696b2a7a5b10ddef5d22ba2c2" title="Covariance matrix in decomposed form.">R</a>.sqrt_mult ( x );
-<a name="l00713"></a>00713                 pom +=<a class="code" href="classbdm_1_1enorm.html#c702a194720853570d08b65482f842c7" title="mean value">mu</a>;
-<a name="l00714"></a>00714                 X.set_col ( i, pom );
-<a name="l00715"></a>00715         }
-<a name="l00716"></a>00716 
-<a name="l00717"></a>00717         <span class="keywordflow">return</span> X;
-<a name="l00718"></a>00718 };
-<a name="l00719"></a>00719 
-<a name="l00720"></a>00720 <span class="comment">// template&lt;class sq_T&gt;</span>
-<a name="l00721"></a>00721 <span class="comment">// double enorm&lt;sq_T&gt;::eval ( const vec &amp;val ) const {</span>
-<a name="l00722"></a>00722 <span class="comment">//      double pdfl,e;</span>
-<a name="l00723"></a>00723 <span class="comment">//      pdfl = evallog ( val );</span>
-<a name="l00724"></a>00724 <span class="comment">//      e = exp ( pdfl );</span>
-<a name="l00725"></a>00725 <span class="comment">//      return e;</span>
-<a name="l00726"></a>00726 <span class="comment">// };</span>
-<a name="l00727"></a>00727 
-<a name="l00728"></a>00728 <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt;
-<a name="l00729"></a><a class="code" href="classbdm_1_1enorm.html#e13aeed5b543b2179bacdc4fa2ae47a3">00729</a> <span class="keywordtype">double</span> <a class="code" href="classbdm_1_1enorm.html" title="Gaussian density with positive definite (decomposed) covariance matrix.">enorm&lt;sq_T&gt;::evallog_nn</a> ( <span class="keyword">const</span> vec &amp;val )<span class="keyword"> const </span>{
-<a name="l00730"></a>00730         <span class="comment">// 1.83787706640935 = log(2pi)</span>
-<a name="l00731"></a>00731         <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="l00732"></a>00732         <span class="keywordflow">return</span>  tmp;
-<a name="l00733"></a>00733 };
-<a name="l00734"></a>00734 
-<a name="l00735"></a>00735 <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt;
-<a name="l00736"></a><a class="code" href="classbdm_1_1enorm.html#25785343aff102cc5df1cab08ba16d32">00736</a> <span class="keyword">inline</span> <span class="keywordtype">double</span> <a class="code" href="classbdm_1_1enorm.html" title="Gaussian density with positive definite (decomposed) covariance matrix.">enorm&lt;sq_T&gt;::lognc</a> ()<span class="keyword"> const </span>{
-<a name="l00737"></a>00737         <span class="comment">// 1.83787706640935 = log(2pi)</span>
-<a name="l00738"></a>00738         <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="l00739"></a>00739         <span class="keywordflow">return</span> tmp;
-<a name="l00740"></a>00740 };
+<a name="l00681"></a>00681 
+<a name="l00683"></a>00683 
+<a name="l00684"></a>00684 <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt;
+<a name="l00685"></a>00685 enorm&lt;sq_T&gt;::enorm ( ) :eEF ( ), mu ( ),R ( ) {};
+<a name="l00686"></a>00686 
+<a name="l00687"></a>00687 <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt;
+<a name="l00688"></a>00688 <span class="keywordtype">void</span> enorm&lt;sq_T&gt;::set_parameters ( <span class="keyword">const</span> vec &amp;mu0, <span class="keyword">const</span> sq_T &amp;R0 ) {
+<a name="l00689"></a>00689 <span class="comment">//Fixme test dimensions of mu0 and R0;</span>
+<a name="l00690"></a>00690         mu = mu0;
+<a name="l00691"></a>00691         R = R0;
+<a name="l00692"></a>00692         <a class="code" href="classbdm_1_1epdf.html#16adac20ec7fe07e1ea0b27d917788ce" title="dimension of the random variable">dim</a> = mu0.length();
+<a name="l00693"></a>00693 };
+<a name="l00694"></a>00694 
+<a name="l00695"></a>00695 <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt;
+<a name="l00696"></a><a class="code" href="classbdm_1_1enorm.html#d2e0d3a1e30ab3ab04df2d0c43ae74a2">00696</a> <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1enorm.html" title="Gaussian density with positive definite (decomposed) covariance matrix.">enorm&lt;sq_T&gt;::dupdate</a> ( mat &amp;v, <span class="keywordtype">double</span> nu ) {
+<a name="l00697"></a>00697         <span class="comment">//</span>
+<a name="l00698"></a>00698 };
+<a name="l00699"></a>00699 
+<a name="l00700"></a>00700 <span class="comment">// template&lt;class sq_T&gt;</span>
+<a name="l00701"></a>00701 <span class="comment">// void enorm&lt;sq_T&gt;::tupdate ( double phi, mat &amp;vbar, double nubar ) {</span>
+<a name="l00702"></a>00702 <span class="comment">//      //</span>
+<a name="l00703"></a>00703 <span class="comment">// };</span>
+<a name="l00704"></a>00704 
+<a name="l00705"></a>00705 <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt;
+<a name="l00706"></a><a class="code" href="classbdm_1_1enorm.html#e1a48f52351ec3a349bd443b713b1766">00706</a> vec <a class="code" href="classbdm_1_1enorm.html" title="Gaussian density with positive definite (decomposed) covariance matrix.">enorm&lt;sq_T&gt;::sample</a>()<span class="keyword"> const </span>{
+<a name="l00707"></a>00707         vec x ( <a class="code" href="classbdm_1_1epdf.html#16adac20ec7fe07e1ea0b27d917788ce" title="dimension of the random variable">dim</a> );
+<a name="l00708"></a>00708 <span class="preprocessor">#pragma omp critical</span>
+<a name="l00709"></a>00709 <span class="preprocessor"></span>        <a class="code" href="namespacebdm.html#c959a7382efbcc31af4b58cf0f0f951a" title="Global Normal_RNG.">NorRNG</a>.sample_vector ( <a class="code" href="classbdm_1_1epdf.html#16adac20ec7fe07e1ea0b27d917788ce" title="dimension of the random variable">dim</a>,x );
+<a name="l00710"></a>00710         vec smp = <a class="code" href="classbdm_1_1enorm.html#2d92dde696b2a7a5b10ddef5d22ba2c2" title="Covariance matrix in decomposed form.">R</a>.sqrt_mult ( x );
+<a name="l00711"></a>00711 
+<a name="l00712"></a>00712         smp += <a class="code" href="classbdm_1_1enorm.html#c702a194720853570d08b65482f842c7" title="mean value">mu</a>;
+<a name="l00713"></a>00713         <span class="keywordflow">return</span> smp;
+<a name="l00714"></a>00714 };
+<a name="l00715"></a>00715 
+<a name="l00716"></a>00716 <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt;
+<a name="l00717"></a>00717 mat <a class="code" href="classbdm_1_1enorm.html" title="Gaussian density with positive definite (decomposed) covariance matrix.">enorm&lt;sq_T&gt;::sample</a> ( <span class="keywordtype">int</span> N )<span class="keyword"> const </span>{
+<a name="l00718"></a>00718         mat X ( <a class="code" href="classbdm_1_1epdf.html#16adac20ec7fe07e1ea0b27d917788ce" title="dimension of the random variable">dim</a>,N );
+<a name="l00719"></a>00719         vec x ( <a class="code" href="classbdm_1_1epdf.html#16adac20ec7fe07e1ea0b27d917788ce" title="dimension of the random variable">dim</a> );
+<a name="l00720"></a>00720         vec pom;
+<a name="l00721"></a>00721         <span class="keywordtype">int</span> i;
+<a name="l00722"></a>00722 
+<a name="l00723"></a>00723         <span class="keywordflow">for</span> ( i=0;i&lt;N;i++ ) {
+<a name="l00724"></a>00724 <span class="preprocessor">#pragma omp critical</span>
+<a name="l00725"></a>00725 <span class="preprocessor"></span>                <a class="code" href="namespacebdm.html#c959a7382efbcc31af4b58cf0f0f951a" title="Global Normal_RNG.">NorRNG</a>.sample_vector ( <a class="code" href="classbdm_1_1epdf.html#16adac20ec7fe07e1ea0b27d917788ce" title="dimension of the random variable">dim</a>,x );
+<a name="l00726"></a>00726                 pom = <a class="code" href="classbdm_1_1enorm.html#2d92dde696b2a7a5b10ddef5d22ba2c2" title="Covariance matrix in decomposed form.">R</a>.sqrt_mult ( x );
+<a name="l00727"></a>00727                 pom +=<a class="code" href="classbdm_1_1enorm.html#c702a194720853570d08b65482f842c7" title="mean value">mu</a>;
+<a name="l00728"></a>00728                 X.set_col ( i, pom );
+<a name="l00729"></a>00729         }
+<a name="l00730"></a>00730 
+<a name="l00731"></a>00731         <span class="keywordflow">return</span> X;
+<a name="l00732"></a>00732 };
+<a name="l00733"></a>00733 
+<a name="l00734"></a>00734 <span class="comment">// template&lt;class sq_T&gt;</span>
+<a name="l00735"></a>00735 <span class="comment">// double enorm&lt;sq_T&gt;::eval ( const vec &amp;val ) const {</span>
+<a name="l00736"></a>00736 <span class="comment">//      double pdfl,e;</span>
+<a name="l00737"></a>00737 <span class="comment">//      pdfl = evallog ( val );</span>
+<a name="l00738"></a>00738 <span class="comment">//      e = exp ( pdfl );</span>
+<a name="l00739"></a>00739 <span class="comment">//      return e;</span>
+<a name="l00740"></a>00740 <span class="comment">// };</span>
 <a name="l00741"></a>00741 
 <a name="l00742"></a>00742 <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt;
-<a name="l00743"></a><a class="code" href="classbdm_1_1mlnorm.html#64d965df6811ff65b94718c427048f4a">00743</a> <a class="code" href="classbdm_1_1mlnorm.html" title="Normal distributed linear function with linear function of mean value;.">mlnorm&lt;sq_T&gt;::mlnorm</a> ( <span class="keyword">const</span> <a class="code" href="classbdm_1_1RV.html" title="Class representing variables, most often random variables.">RV</a> &amp;rv0, <span class="keyword">const</span> <a class="code" href="classbdm_1_1RV.html" title="Class representing variables, most often random variables.">RV</a> &amp;rvc0 ) :<a class="code" href="classbdm_1_1mEF.html" title="Exponential family model.">mEF</a> ( rv0,rvc0 ),<a class="code" href="classbdm_1_1epdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a> ( rv0 ),A ( rv0.count(),rv0.count() ),<a class="code" href="classbdm_1_1enorm.html#766127847e9482aea9226ea157295ea2" title="returns a pointer to the internal mean value. Use with Care!">_mu</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#766127847e9482aea9226ea157295ea2" title="returns a pointer to the internal mean value. Use with Care!">_mu</a>() ) {
-<a name="l00744"></a>00744         <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="l00745"></a>00745 }
-<a name="l00746"></a>00746 
-<a name="l00747"></a>00747 <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt;
-<a name="l00748"></a><a class="code" href="classbdm_1_1mlnorm.html#5d18dec3167584338a4775c1d165d96f">00748</a> <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1mlnorm.html" title="Normal distributed linear function with linear function of mean value;.">mlnorm&lt;sq_T&gt;::set_parameters</a> ( <span class="keyword">const</span> mat &amp;A0, <span class="keyword">const</span> vec &amp;mu0, <span class="keyword">const</span> sq_T &amp;R0 ) {
-<a name="l00749"></a>00749         <a class="code" href="classbdm_1_1epdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a>.set_parameters ( zeros ( <a class="code" href="classbdm_1_1mpdf.html#9bcfb45435d30983f436d41c298cbb51" title="modeled random variable">rv</a>.<a class="code" href="classbdm_1_1RV.html#2174751a00ce19f941edd2c1a861be67" title="Return number of scalars in the RV.">count</a>() ),R0 );
-<a name="l00750"></a>00750         A = A0;
-<a name="l00751"></a>00751         mu_const = mu0;
-<a name="l00752"></a>00752 }
-<a name="l00753"></a>00753 
-<a name="l00754"></a>00754 <span class="comment">// template&lt;class sq_T&gt;</span>
-<a name="l00755"></a>00755 <span class="comment">// vec mlnorm&lt;sq_T&gt;::samplecond (const  vec &amp;cond, double &amp;lik ) {</span>
-<a name="l00756"></a>00756 <span class="comment">//      this-&gt;condition ( cond );</span>
-<a name="l00757"></a>00757 <span class="comment">//      vec smp = epdf.sample();</span>
-<a name="l00758"></a>00758 <span class="comment">//      lik = epdf.eval ( smp );</span>
-<a name="l00759"></a>00759 <span class="comment">//      return smp;</span>
-<a name="l00760"></a>00760 <span class="comment">// }</span>
-<a name="l00761"></a>00761 
-<a name="l00762"></a>00762 <span class="comment">// template&lt;class sq_T&gt;</span>
-<a name="l00763"></a>00763 <span class="comment">// mat mlnorm&lt;sq_T&gt;::samplecond (const vec &amp;cond, vec &amp;lik, int n ) {</span>
-<a name="l00764"></a>00764 <span class="comment">//      int i;</span>
-<a name="l00765"></a>00765 <span class="comment">//      int dim = rv.count();</span>
-<a name="l00766"></a>00766 <span class="comment">//      mat Smp ( dim,n );</span>
-<a name="l00767"></a>00767 <span class="comment">//      vec smp ( dim );</span>
-<a name="l00768"></a>00768 <span class="comment">//      this-&gt;condition ( cond );</span>
-<a name="l00769"></a>00769 <span class="comment">//</span>
-<a name="l00770"></a>00770 <span class="comment">//      for ( i=0; i&lt;n; i++ ) {</span>
-<a name="l00771"></a>00771 <span class="comment">//              smp = epdf.sample();</span>
-<a name="l00772"></a>00772 <span class="comment">//              lik ( i ) = epdf.eval ( smp );</span>
-<a name="l00773"></a>00773 <span class="comment">//              Smp.set_col ( i ,smp );</span>
-<a name="l00774"></a>00774 <span class="comment">//      }</span>
-<a name="l00775"></a>00775 <span class="comment">//</span>
-<a name="l00776"></a>00776 <span class="comment">//      return Smp;</span>
+<a name="l00743"></a><a class="code" href="classbdm_1_1enorm.html#e13aeed5b543b2179bacdc4fa2ae47a3">00743</a> <span class="keywordtype">double</span> <a class="code" href="classbdm_1_1enorm.html" title="Gaussian density with positive definite (decomposed) covariance matrix.">enorm&lt;sq_T&gt;::evallog_nn</a> ( <span class="keyword">const</span> vec &amp;val )<span class="keyword"> const </span>{
+<a name="l00744"></a>00744         <span class="comment">// 1.83787706640935 = log(2pi)</span>
+<a name="l00745"></a>00745         <span class="keywordtype">double</span> tmp=-0.5* ( <a class="code" href="classbdm_1_1enorm.html#2d92dde696b2a7a5b10ddef5d22ba2c2" title="Covariance matrix in decomposed form.">R</a>.invqform ( <a class="code" href="classbdm_1_1enorm.html#c702a194720853570d08b65482f842c7" title="mean value">mu</a>-val ) );<span class="comment">// - lognc();</span>
+<a name="l00746"></a>00746         <span class="keywordflow">return</span>  tmp;
+<a name="l00747"></a>00747 };
+<a name="l00748"></a>00748 
+<a name="l00749"></a>00749 <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt;
+<a name="l00750"></a><a class="code" href="classbdm_1_1enorm.html#25785343aff102cc5df1cab08ba16d32">00750</a> <span class="keyword">inline</span> <span class="keywordtype">double</span> <a class="code" href="classbdm_1_1enorm.html" title="Gaussian density with positive definite (decomposed) covariance matrix.">enorm&lt;sq_T&gt;::lognc</a> ()<span class="keyword"> const </span>{
+<a name="l00751"></a>00751         <span class="comment">// 1.83787706640935 = log(2pi)</span>
+<a name="l00752"></a>00752         <span class="keywordtype">double</span> tmp=0.5* ( <a class="code" href="classbdm_1_1enorm.html#2d92dde696b2a7a5b10ddef5d22ba2c2" title="Covariance matrix in decomposed form.">R</a>.cols() * 1.83787706640935 +<a class="code" href="classbdm_1_1enorm.html#2d92dde696b2a7a5b10ddef5d22ba2c2" title="Covariance matrix in decomposed form.">R</a>.logdet() );
+<a name="l00753"></a>00753         <span class="keywordflow">return</span> tmp;
+<a name="l00754"></a>00754 };
+<a name="l00755"></a>00755 
+<a name="l00756"></a>00756 <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt;
+<a name="l00757"></a><a class="code" href="classbdm_1_1mlnorm.html#2dcc06a3dd71f038efbe14dc34d937ae">00757</a> <a class="code" href="classbdm_1_1mlnorm.html" title="Normal distributed linear function with linear function of mean value;.">mlnorm&lt;sq_T&gt;::mlnorm</a> ( ) :<a class="code" href="classbdm_1_1mEF.html" title="Exponential family model.">mEF</a> (),<a class="code" href="classbdm_1_1epdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a> ( ),A ( ),_mu ( <a class="code" href="classbdm_1_1epdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a>._mu() ) {
+<a name="l00758"></a>00758         <a class="code" href="classbdm_1_1mpdf.html#5eea43c56d38e4441bfb30270db949c0" title="pointer to internal epdf">ep</a> =&amp;<a class="code" href="classbdm_1_1epdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a>;
+<a name="l00759"></a>00759 }
+<a name="l00760"></a>00760 
+<a name="l00761"></a>00761 <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt;
+<a name="l00762"></a><a class="code" href="classbdm_1_1mlnorm.html#5d18dec3167584338a4775c1d165d96f">00762</a> <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1mlnorm.html" title="Normal distributed linear function with linear function of mean value;.">mlnorm&lt;sq_T&gt;::set_parameters</a> ( <span class="keyword">const</span> mat &amp;A0, <span class="keyword">const</span> vec &amp;mu0, <span class="keyword">const</span> sq_T &amp;R0 ) {
+<a name="l00763"></a>00763         it_assert_debug ( A0.rows() ==mu0.length(),<span class="stringliteral">""</span> );
+<a name="l00764"></a>00764         it_assert_debug ( A0.rows() ==R0.rows(),<span class="stringliteral">""</span> );
+<a name="l00765"></a>00765 
+<a name="l00766"></a>00766         <a class="code" href="classbdm_1_1epdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a>.set_parameters ( zeros ( A.rows() ),R0 );
+<a name="l00767"></a>00767         A = A0;
+<a name="l00768"></a>00768         mu_const = mu0;
+<a name="l00769"></a>00769 }
+<a name="l00770"></a>00770 
+<a name="l00771"></a>00771 <span class="comment">// template&lt;class sq_T&gt;</span>
+<a name="l00772"></a>00772 <span class="comment">// vec mlnorm&lt;sq_T&gt;::samplecond (const  vec &amp;cond, double &amp;lik ) {</span>
+<a name="l00773"></a>00773 <span class="comment">//      this-&gt;condition ( cond );</span>
+<a name="l00774"></a>00774 <span class="comment">//      vec smp = epdf.sample();</span>
+<a name="l00775"></a>00775 <span class="comment">//      lik = epdf.eval ( smp );</span>
+<a name="l00776"></a>00776 <span class="comment">//      return smp;</span>
 <a name="l00777"></a>00777 <span class="comment">// }</span>
 <a name="l00778"></a>00778 
-<a name="l00779"></a>00779 <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt;
-<a name="l00780"></a><a class="code" href="classbdm_1_1mlnorm.html#0dafc0196e7e07fd06dc6716e0e18bc2">00780</a> <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1mlnorm.html" title="Normal distributed linear function with linear function of mean value;.">mlnorm&lt;sq_T&gt;::condition</a> ( <span class="keyword">const</span> vec &amp;cond ) {
-<a name="l00781"></a>00781         _mu = A*cond + mu_const;
-<a name="l00782"></a>00782 <span class="comment">//R is already assigned;</span>
-<a name="l00783"></a>00783 }
-<a name="l00784"></a>00784 
-<a name="l00785"></a>00785 <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt;
-<a name="l00786"></a><a class="code" href="classbdm_1_1enorm.html#cd02d76e9d4f96bdd3fa6b604e273039">00786</a> <a class="code" href="classbdm_1_1epdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a>* <a class="code" href="classbdm_1_1enorm.html" title="Gaussian density with positive definite (decomposed) covariance matrix.">enorm&lt;sq_T&gt;::marginal</a> ( <span class="keyword">const</span> <a class="code" href="classbdm_1_1RV.html" title="Class representing variables, most often random variables.">RV</a> &amp;rvn )<span class="keyword"> const </span>{
-<a name="l00787"></a>00787         ivec irvn = rvn.<a class="code" href="classbdm_1_1RV.html#cbebdb5e0d30101a6eb63550ef701c55">dataind</a> ( <a class="code" href="classbdm_1_1epdf.html#62c5b8ff71d9ebe6cd58d3c342eb1dc8" title="Identified of the random variable.">rv</a> );
-<a name="l00788"></a>00788 
-<a name="l00789"></a>00789         sq_T Rn ( <a class="code" href="classbdm_1_1enorm.html#2d92dde696b2a7a5b10ddef5d22ba2c2" title="Covariance matrix in decomposed form.">R</a>,irvn );
-<a name="l00790"></a>00790         <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> ( rvn );
-<a name="l00791"></a>00791         tmp-&gt;<a class="code" href="classbdm_1_1enorm.html#b8322f2c11560871dd922c660f4771bb" title="Set mean value mu and covariance R.">set_parameters</a> ( <a class="code" href="classbdm_1_1enorm.html#c702a194720853570d08b65482f842c7" title="mean value">mu</a> ( irvn ), Rn );
-<a name="l00792"></a>00792         <span class="keywordflow">return</span> tmp;
-<a name="l00793"></a>00793 }
-<a name="l00794"></a>00794 
-<a name="l00795"></a>00795 <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt;
-<a name="l00796"></a><a class="code" href="classbdm_1_1enorm.html#baea4d49c657342b58297d68cda16d26">00796</a> <a class="code" href="classbdm_1_1mpdf.html" title="Conditional probability density, e.g. modeling some dependencies.">mpdf</a>* <a class="code" href="classbdm_1_1enorm.html" title="Gaussian density with positive definite (decomposed) covariance matrix.">enorm&lt;sq_T&gt;::condition</a> ( <span class="keyword">const</span> <a class="code" href="classbdm_1_1RV.html" title="Class representing variables, most often random variables.">RV</a> &amp;rvn )<span class="keyword"> const </span>{
-<a name="l00797"></a>00797 
-<a name="l00798"></a>00798         <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="Identified 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="l00799"></a>00799         it_assert_debug ( ( rvc.<a class="code" href="classbdm_1_1RV.html#2174751a00ce19f941edd2c1a861be67" title="Return number of scalars in the RV.">count</a>() +rvn.<a class="code" href="classbdm_1_1RV.html#2174751a00ce19f941edd2c1a861be67" title="Return number of scalars in the RV.">count</a>() ==<a class="code" href="classbdm_1_1epdf.html#62c5b8ff71d9ebe6cd58d3c342eb1dc8" title="Identified of the random variable.">rv</a>.<a class="code" href="classbdm_1_1RV.html#2174751a00ce19f941edd2c1a861be67" title="Return number of scalars in the RV.">count</a>() ),<span class="stringliteral">"wrong rvn"</span> );
-<a name="l00800"></a>00800         <span class="comment">//Permutation vector of the new R</span>
-<a name="l00801"></a>00801         ivec irvn = rvn.<a class="code" href="classbdm_1_1RV.html#cbebdb5e0d30101a6eb63550ef701c55">dataind</a> ( <a class="code" href="classbdm_1_1epdf.html#62c5b8ff71d9ebe6cd58d3c342eb1dc8" title="Identified of the random variable.">rv</a> );
-<a name="l00802"></a>00802         ivec irvc = rvc.<a class="code" href="classbdm_1_1RV.html#cbebdb5e0d30101a6eb63550ef701c55">dataind</a> ( <a class="code" href="classbdm_1_1epdf.html#62c5b8ff71d9ebe6cd58d3c342eb1dc8" title="Identified of the random variable.">rv</a> );
-<a name="l00803"></a>00803         ivec perm=<a class="code" href="namespacebdm.html#b9016687c0e874ca5cdcf75ae28811aa" title="Concat two random variables.">concat</a> ( irvn , irvc );
-<a name="l00804"></a>00804         sq_T Rn ( <a class="code" href="classbdm_1_1enorm.html#2d92dde696b2a7a5b10ddef5d22ba2c2" title="Covariance matrix in decomposed form.">R</a>,perm );
+<a name="l00779"></a>00779 <span class="comment">// template&lt;class sq_T&gt;</span>
+<a name="l00780"></a>00780 <span class="comment">// mat mlnorm&lt;sq_T&gt;::samplecond (const vec &amp;cond, vec &amp;lik, int n ) {</span>
+<a name="l00781"></a>00781 <span class="comment">//      int i;</span>
+<a name="l00782"></a>00782 <span class="comment">//      int dim = rv.count();</span>
+<a name="l00783"></a>00783 <span class="comment">//      mat Smp ( dim,n );</span>
+<a name="l00784"></a>00784 <span class="comment">//      vec smp ( dim );</span>
+<a name="l00785"></a>00785 <span class="comment">//      this-&gt;condition ( cond );</span>
+<a name="l00786"></a>00786 <span class="comment">//</span>
+<a name="l00787"></a>00787 <span class="comment">//      for ( i=0; i&lt;n; i++ ) {</span>
+<a name="l00788"></a>00788 <span class="comment">//              smp = epdf.sample();</span>
+<a name="l00789"></a>00789 <span class="comment">//              lik ( i ) = epdf.eval ( smp );</span>
+<a name="l00790"></a>00790 <span class="comment">//              Smp.set_col ( i ,smp );</span>
+<a name="l00791"></a>00791 <span class="comment">//      }</span>
+<a name="l00792"></a>00792 <span class="comment">//</span>
+<a name="l00793"></a>00793 <span class="comment">//      return Smp;</span>
+<a name="l00794"></a>00794 <span class="comment">// }</span>
+<a name="l00795"></a>00795 
+<a name="l00796"></a>00796 <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt;
+<a name="l00797"></a><a class="code" href="classbdm_1_1mlnorm.html#0dafc0196e7e07fd06dc6716e0e18bc2">00797</a> <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1mlnorm.html" title="Normal distributed linear function with linear function of mean value;.">mlnorm&lt;sq_T&gt;::condition</a> ( <span class="keyword">const</span> vec &amp;cond ) {
+<a name="l00798"></a>00798         _mu = A*cond + mu_const;
+<a name="l00799"></a>00799 <span class="comment">//R is already assigned;</span>
+<a name="l00800"></a>00800 }
+<a name="l00801"></a>00801 
+<a name="l00802"></a>00802 <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt;
+<a name="l00803"></a><a class="code" href="classbdm_1_1enorm.html#cd02d76e9d4f96bdd3fa6b604e273039">00803</a> <a class="code" href="classbdm_1_1epdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a>* <a class="code" href="classbdm_1_1enorm.html" title="Gaussian density with positive definite (decomposed) covariance matrix.">enorm&lt;sq_T&gt;::marginal</a> ( <span class="keyword">const</span> <a class="code" href="classbdm_1_1RV.html" title="Class representing variables, most often random variables.">RV</a> &amp;rvn )<span class="keyword"> const </span>{
+<a name="l00804"></a>00804         ivec irvn = rvn.<a class="code" href="classbdm_1_1RV.html#cbebdb5e0d30101a6eb63550ef701c55">dataind</a> ( <a class="code" href="classbdm_1_1epdf.html#62c5b8ff71d9ebe6cd58d3c342eb1dc8" title="Description of the random variable.">rv</a> );
 <a name="l00805"></a>00805 
-<a name="l00806"></a>00806         <span class="comment">//fixme - could this be done in general for all sq_T?</span>
-<a name="l00807"></a>00807         mat S=Rn.to_mat();
-<a name="l00808"></a>00808         <span class="comment">//fixme</span>
-<a name="l00809"></a>00809         <span class="keywordtype">int</span> n=rvn.<a class="code" href="classbdm_1_1RV.html#2174751a00ce19f941edd2c1a861be67" title="Return number of scalars in the RV.">count</a>()-1;
-<a name="l00810"></a>00810         <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="l00811"></a>00811         mat S11 = S.get ( 0,n, 0, n );
-<a name="l00812"></a>00812         mat S12 = S.get ( 0, n , rvn.<a class="code" href="classbdm_1_1RV.html#2174751a00ce19f941edd2c1a861be67" title="Return number of scalars in the RV.">count</a>(), end );
-<a name="l00813"></a>00813         mat S22 = S.get ( rvn.<a class="code" href="classbdm_1_1RV.html#2174751a00ce19f941edd2c1a861be67" title="Return number of scalars in the RV.">count</a>(), end, rvn.<a class="code" href="classbdm_1_1RV.html#2174751a00ce19f941edd2c1a861be67" title="Return number of scalars in the RV.">count</a>(), end );
-<a name="l00814"></a>00814 
-<a name="l00815"></a>00815         vec mu1 = <a class="code" href="classbdm_1_1enorm.html#c702a194720853570d08b65482f842c7" title="mean value">mu</a> ( irvn );
-<a name="l00816"></a>00816         vec mu2 = <a class="code" href="classbdm_1_1enorm.html#c702a194720853570d08b65482f842c7" title="mean value">mu</a> ( irvc );
-<a name="l00817"></a>00817         mat A=S12*inv ( S22 );
-<a name="l00818"></a>00818         sq_T R_n ( S11 - A *S12.T() );
-<a name="l00819"></a>00819 
-<a name="l00820"></a>00820         <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> ( rvn,rvc );
-<a name="l00821"></a>00821 
-<a name="l00822"></a>00822         tmp-&gt;set_parameters ( A,mu1-A*mu2,R_n );
-<a name="l00823"></a>00823         <span class="keywordflow">return</span> tmp;
-<a name="l00824"></a>00824 }
+<a name="l00806"></a>00806         sq_T Rn ( <a class="code" href="classbdm_1_1enorm.html#2d92dde696b2a7a5b10ddef5d22ba2c2" title="Covariance matrix in decomposed form.">R</a>,irvn );
+<a name="l00807"></a>00807         <a class="code" href="classbdm_1_1enorm.html" title="Gaussian density with positive definite (decomposed) covariance matrix.">enorm&lt;sq_T&gt;</a>* tmp = <span class="keyword">new</span> <a class="code" href="classbdm_1_1enorm.html" title="Gaussian density with positive definite (decomposed) covariance matrix.">enorm&lt;sq_T&gt;</a> ( );
+<a name="l00808"></a>00808         tmp-&gt;<a class="code" href="classbdm_1_1epdf.html#f423e28448dbb69ef4905295ec8de8ff" title="Name its rv.">set_rv</a> ( rvn );
+<a name="l00809"></a>00809         tmp-&gt;<a class="code" href="classbdm_1_1enorm.html#b8322f2c11560871dd922c660f4771bb">set_parameters</a> ( <a class="code" href="classbdm_1_1enorm.html#c702a194720853570d08b65482f842c7" title="mean value">mu</a> ( irvn ), Rn );
+<a name="l00810"></a>00810         <span class="keywordflow">return</span> tmp;
+<a name="l00811"></a>00811 }
+<a name="l00812"></a>00812 
+<a name="l00813"></a>00813 <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt;
+<a name="l00814"></a><a class="code" href="classbdm_1_1enorm.html#baea4d49c657342b58297d68cda16d26">00814</a> <a class="code" href="classbdm_1_1mpdf.html" title="Conditional probability density, e.g. modeling some dependencies.">mpdf</a>* <a class="code" href="classbdm_1_1enorm.html" title="Gaussian density with positive definite (decomposed) covariance matrix.">enorm&lt;sq_T&gt;::condition</a> ( <span class="keyword">const</span> <a class="code" href="classbdm_1_1RV.html" title="Class representing variables, most often random variables.">RV</a> &amp;rvn )<span class="keyword"> const </span>{
+<a name="l00815"></a>00815 
+<a name="l00816"></a>00816         it_assert_debug ( <a class="code" href="classbdm_1_1epdf.html#c4b863ff84c7a4882fb3ad18556027f9" title="True if rv is assigned.">isnamed</a>(),<span class="stringliteral">""</span> );
+<a name="l00817"></a>00817 
+<a name="l00818"></a>00818         <a class="code" href="classbdm_1_1RV.html" title="Class representing variables, most often random variables.">RV</a> rvc = <a class="code" href="classbdm_1_1epdf.html#62c5b8ff71d9ebe6cd58d3c342eb1dc8" title="Description of the random variable.">rv</a>.<a class="code" href="classbdm_1_1RV.html#aec44dabdf0a6d90fbae95e1356eda39" title="Subtract another variable from the current one.">subt</a> ( rvn );
+<a name="l00819"></a>00819         it_assert_debug ( ( rvc.<a class="code" href="classbdm_1_1RV.html#de30156104f61d86c94f758861418089">_dsize</a>() +rvn.<a class="code" href="classbdm_1_1RV.html#de30156104f61d86c94f758861418089">_dsize</a>() ==<a class="code" href="classbdm_1_1epdf.html#62c5b8ff71d9ebe6cd58d3c342eb1dc8" title="Description of the random variable.">rv</a>.<a class="code" href="classbdm_1_1RV.html#de30156104f61d86c94f758861418089">_dsize</a>() ),<span class="stringliteral">"wrong rvn"</span> );
+<a name="l00820"></a>00820         <span class="comment">//Permutation vector of the new R</span>
+<a name="l00821"></a>00821         ivec irvn = rvn.<a class="code" href="classbdm_1_1RV.html#cbebdb5e0d30101a6eb63550ef701c55">dataind</a> ( <a class="code" href="classbdm_1_1epdf.html#62c5b8ff71d9ebe6cd58d3c342eb1dc8" title="Description of the random variable.">rv</a> );
+<a name="l00822"></a>00822         ivec irvc = rvc.<a class="code" href="classbdm_1_1RV.html#cbebdb5e0d30101a6eb63550ef701c55">dataind</a> ( <a class="code" href="classbdm_1_1epdf.html#62c5b8ff71d9ebe6cd58d3c342eb1dc8" title="Description of the random variable.">rv</a> );
+<a name="l00823"></a>00823         ivec perm=<a class="code" href="namespacebdm.html#b9016687c0e874ca5cdcf75ae28811aa" title="Concat two random variables.">concat</a> ( irvn , irvc );
+<a name="l00824"></a>00824         sq_T Rn ( <a class="code" href="classbdm_1_1enorm.html#2d92dde696b2a7a5b10ddef5d22ba2c2" title="Covariance matrix in decomposed form.">R</a>,perm );
 <a name="l00825"></a>00825 
-<a name="l00827"></a>00827 
-<a name="l00828"></a>00828 <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt;
-<a name="l00829"></a>00829 std::ostream &amp;operator&lt;&lt; ( std::ostream &amp;os,  mlnorm&lt;sq_T&gt; &amp;ml ) {
-<a name="l00830"></a>00830         os &lt;&lt; <span class="stringliteral">"A:"</span>&lt;&lt; ml.A&lt;&lt;endl;
-<a name="l00831"></a>00831         os &lt;&lt; <span class="stringliteral">"mu:"</span>&lt;&lt; ml.mu_const&lt;&lt;endl;
-<a name="l00832"></a>00832         os &lt;&lt; <span class="stringliteral">"R:"</span> &lt;&lt; ml.epdf._R().to_mat() &lt;&lt;endl;
-<a name="l00833"></a>00833         <span class="keywordflow">return</span> os;
-<a name="l00834"></a>00834 };
-<a name="l00835"></a>00835 
-<a name="l00836"></a>00836 }
-<a name="l00837"></a>00837 <span class="preprocessor">#endif //EF_H</span>
+<a name="l00826"></a>00826         <span class="comment">//fixme - could this be done in general for all sq_T?</span>
+<a name="l00827"></a>00827         mat S=Rn.to_mat();
+<a name="l00828"></a>00828         <span class="comment">//fixme</span>
+<a name="l00829"></a>00829         <span class="keywordtype">int</span> n=rvn.<a class="code" href="classbdm_1_1RV.html#de30156104f61d86c94f758861418089">_dsize</a>()-1;
+<a name="l00830"></a>00830         <span class="keywordtype">int</span> end=<a class="code" href="classbdm_1_1enorm.html#2d92dde696b2a7a5b10ddef5d22ba2c2" title="Covariance matrix in decomposed form.">R</a>.rows()-1;
+<a name="l00831"></a>00831         mat S11 = S.get ( 0,n, 0, n );
+<a name="l00832"></a>00832         mat S12 = S.get ( 0, n , rvn.<a class="code" href="classbdm_1_1RV.html#de30156104f61d86c94f758861418089">_dsize</a>(), end );
+<a name="l00833"></a>00833         mat S22 = S.get ( rvn.<a class="code" href="classbdm_1_1RV.html#de30156104f61d86c94f758861418089">_dsize</a>(), end, rvn.<a class="code" href="classbdm_1_1RV.html#de30156104f61d86c94f758861418089">_dsize</a>(), end );
+<a name="l00834"></a>00834 
+<a name="l00835"></a>00835         vec mu1 = <a class="code" href="classbdm_1_1enorm.html#c702a194720853570d08b65482f842c7" title="mean value">mu</a> ( irvn );
+<a name="l00836"></a>00836         vec mu2 = <a class="code" href="classbdm_1_1enorm.html#c702a194720853570d08b65482f842c7" title="mean value">mu</a> ( irvc );
+<a name="l00837"></a>00837         mat A=S12*inv ( S22 );
+<a name="l00838"></a>00838         sq_T R_n ( S11 - A *S12.T() );
+<a name="l00839"></a>00839 
+<a name="l00840"></a>00840         <a class="code" href="classbdm_1_1mlnorm.html" title="Normal distributed linear function with linear function of mean value;.">mlnorm&lt;sq_T&gt;</a>* tmp=<span class="keyword">new</span> <a class="code" href="classbdm_1_1mlnorm.html" title="Normal distributed linear function with linear function of mean value;.">mlnorm&lt;sq_T&gt;</a> ( );
+<a name="l00841"></a>00841 
+<a name="l00842"></a>00842         tmp-&gt;set_parameters ( A,mu1-A*mu2,R_n );
+<a name="l00843"></a>00843         <span class="keywordflow">return</span> tmp;
+<a name="l00844"></a>00844 }
+<a name="l00845"></a>00845 
+<a name="l00847"></a>00847 
+<a name="l00848"></a>00848 <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt;
+<a name="l00849"></a>00849 std::ostream &amp;operator&lt;&lt; ( std::ostream &amp;os,  mlnorm&lt;sq_T&gt; &amp;ml ) {
+<a name="l00850"></a>00850         os &lt;&lt; <span class="stringliteral">"A:"</span>&lt;&lt; ml.A&lt;&lt;endl;
+<a name="l00851"></a>00851         os &lt;&lt; <span class="stringliteral">"mu:"</span>&lt;&lt; ml.mu_const&lt;&lt;endl;
+<a name="l00852"></a>00852         os &lt;&lt; <span class="stringliteral">"R:"</span> &lt;&lt; ml.epdf._R().to_mat() &lt;&lt;endl;
+<a name="l00853"></a>00853         <span class="keywordflow">return</span> os;
+<a name="l00854"></a>00854 };
+<a name="l00855"></a>00855 
+<a name="l00856"></a>00856 }
+<a name="l00857"></a>00857 <span class="preprocessor">#endif //EF_H</span>
 </pre></div></div>
-<hr size="1"><address style="text-align: right;"><small>Generated on Wed Feb 11 10:20:05 2009 for mixpp by&nbsp;
+<hr size="1"><address style="text-align: right;"><small>Generated on Wed Feb 11 23:33:55 2009 for mixpp by&nbsp;
 <a href="http://www.doxygen.org/index.html">
 <img src="doxygen.png" alt="doxygen" align="middle" border="0"></a> 1.5.6 </small></address>