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[145]18<h1>work/git/mixpp/bdm/stat/libEF.h</h1><a href="libEF_8h.html">Go to the documentation of this file.</a><div class="fragment"><pre class="fragment"><a name="l00001"></a>00001
[8]19<a name="l00013"></a>00013 <span class="preprocessor">#ifndef EF_H</span>
20<a name="l00014"></a>00014 <span class="preprocessor"></span><span class="preprocessor">#define EF_H</span>
21<a name="l00015"></a>00015 <span class="preprocessor"></span>
22<a name="l00016"></a>00016 <span class="preprocessor">#include &lt;itpp/itbase.h&gt;</span>
[19]23<a name="l00017"></a>00017 <span class="preprocessor">#include "../math/libDC.h"</span>
24<a name="l00018"></a>00018 <span class="preprocessor">#include "<a class="code" href="libBM_8h.html" title="Bayesian Models (bm) that use Bayes rule to learn from observations.">libBM.h</a>"</span>
[32]25<a name="l00019"></a>00019 <span class="preprocessor">#include "../itpp_ext.h"</span>
26<a name="l00020"></a>00020 <span class="comment">//#include &lt;std&gt;</span>
27<a name="l00021"></a>00021
[255]28<a name="l00022"></a>00022 <span class="keyword">namespace </span>bdm{
[32]29<a name="l00023"></a>00023
30<a name="l00024"></a>00024
31<a name="l00026"></a>00026 <span class="keyword">extern</span> Uniform_RNG UniRNG;
[33]32<a name="l00028"></a>00028 <span class="keyword">extern</span> Normal_RNG NorRNG;
33<a name="l00030"></a>00030 <span class="keyword">extern</span> <a class="code" href="classitpp_1_1Gamma__RNG.html" title="Gamma distribution.">Gamma_RNG</a> GamRNG;
34<a name="l00031"></a>00031
[255]35<a name="l00038"></a><a class="code" href="classbdm_1_1eEF.html">00038</a> <span class="keyword">class </span><a class="code" href="classbdm_1_1eEF.html" title="General conjugate exponential family posterior density.">eEF</a> : <span class="keyword">public</span> <a class="code" href="classbdm_1_1epdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a> {
[99]36<a name="l00039"></a>00039 <span class="keyword">public</span>:
37<a name="l00040"></a>00040 <span class="comment">//      eEF() :epdf() {};</span>
[255]38<a name="l00042"></a><a class="code" href="classbdm_1_1eEF.html#1e92e3f94e594edb20adfa81ae9e2959">00042</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 ) {};
39<a name="l00044"></a>00044         <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;
40<a name="l00046"></a><a class="code" href="classbdm_1_1eEF.html#deef7d6273ba4d5a5cf0bbd91ec7277a">00046</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> );};
41<a name="l00048"></a><a class="code" href="classbdm_1_1eEF.html#a4135778ecd9ab774762936c82a097c6">00048</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;};
42<a name="l00050"></a><a class="code" href="classbdm_1_1eEF.html#a36d06ecdd6f4c79dc122510eaccc692">00050</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;}
43<a name="l00052"></a><a class="code" href="classbdm_1_1eEF.html#79a7c8ea8c02e45d410bd1d7ffd72b41">00052</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>{
[171]44<a name="l00053"></a>00053                 vec x ( Val.cols() );
[255]45<a name="l00054"></a>00054                 <span class="keywordflow">for</span> ( <span class="keywordtype">int</span> i=0;i&lt;Val.cols();i++ ) {x ( i ) =<a class="code" href="classbdm_1_1eEF.html#a4135778ecd9ab774762936c82a097c6" title="Evaluate normalized log-probability.">evallog_nn</a> ( Val.get_col ( i ) ) ;}
46<a name="l00055"></a>00055                 <span class="keywordflow">return</span> x-<a class="code" href="classbdm_1_1eEF.html#cd678fc9b02007a4b8d6692e746f1bf8" title="logarithm of the normalizing constant, ">lognc</a>();
[171]47<a name="l00056"></a>00056         }
[255]48<a name="l00058"></a><a class="code" href="classbdm_1_1eEF.html#cf38af29e8e3d650c640509a52396053">00058</a>         <span class="keyword">virtual</span> <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1eEF.html#cf38af29e8e3d650c640509a52396053" title="Power of the density, used e.g. to flatten the density.">pow</a> ( <span class="keywordtype">double</span> p ) {it_error ( <span class="stringliteral">"Not implemented"</span> );};
[171]49<a name="l00059"></a>00059 };
50<a name="l00060"></a>00060
[255]51<a name="l00067"></a><a class="code" href="classbdm_1_1mEF.html">00067</a> <span class="keyword">class </span><a class="code" href="classbdm_1_1mEF.html" title="Exponential family model.">mEF</a> : <span class="keyword">public</span> <a class="code" href="classbdm_1_1mpdf.html" title="Conditional probability density, e.g. modeling some dependencies.">mpdf</a> {
[171]52<a name="l00068"></a>00068
53<a name="l00069"></a>00069 <span class="keyword">public</span>:
[255]54<a name="l00071"></a><a class="code" href="classbdm_1_1mEF.html#f6647b16e9c99b8a7d7df93374ef90f3">00071</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 ) {};
[171]55<a name="l00072"></a>00072 };
56<a name="l00073"></a>00073
[255]57<a name="l00075"></a><a class="code" href="classbdm_1_1BMEF.html">00075</a> <span class="keyword">class </span><a class="code" href="classbdm_1_1BMEF.html" title="Estimator for Exponential family.">BMEF</a> : <span class="keyword">public</span> <a class="code" href="classbdm_1_1BM.html" title="Bayesian Model of the world, i.e. all uncertainty is modeled by probabilities.">BM</a> {
[171]58<a name="l00076"></a>00076 <span class="keyword">protected</span>:
[255]59<a name="l00078"></a><a class="code" href="classbdm_1_1BMEF.html#1331865e10fb1ccef65bb4c47fa3be64">00078</a>         <span class="keywordtype">double</span> <a class="code" href="classbdm_1_1BMEF.html#1331865e10fb1ccef65bb4c47fa3be64" title="forgetting factor">frg</a>;
60<a name="l00080"></a><a class="code" href="classbdm_1_1BMEF.html#06e7b3ac03e10017d4288c76888e2865">00080</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>;
[171]61<a name="l00081"></a>00081 <span class="keyword">public</span>:
[255]62<a name="l00083"></a><a class="code" href="classbdm_1_1BMEF.html#73bccd1d8142d4d330e35637ca30decc">00083</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 the world, 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 ) {}
63<a name="l00085"></a><a class="code" href="classbdm_1_1BMEF.html#9662379513101405e159e76717104e62">00085</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 the world, 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> ) {}
64<a name="l00087"></a><a class="code" href="classbdm_1_1BMEF.html#d2b528b7a41ca67163152142f5404051">00087</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> );};
65<a name="l00089"></a><a class="code" href="classbdm_1_1BMEF.html#bf58deb99af2a6cc674f13ff90300de6">00089</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 ) {};
[171]66<a name="l00090"></a>00090         <span class="comment">//original Bayes</span>
[255]67<a name="l00091"></a>00091         <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1BMEF.html#bf58deb99af2a6cc674f13ff90300de6" title="Weighted update of sufficient statistics (Bayes rule).">bayes</a> ( <span class="keyword">const</span> vec &amp;dt );
68<a name="l00093"></a><a class="code" href="classbdm_1_1BMEF.html#b2916a2e71a958665054473124d5e749">00093</a>         <span class="keyword">virtual</span> <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1BMEF.html#b2916a2e71a958665054473124d5e749" title="Flatten the posterior according to the given BMEF (of the same type!).">flatten</a> ( <span class="keyword">const</span> <a class="code" href="classbdm_1_1BMEF.html" title="Estimator for Exponential family.">BMEF</a> * B ) {it_error ( <span class="stringliteral">"Not implemented"</span> );}
[210]69<a name="l00095"></a>00095 <span class="comment">//      virtual void flatten ( double nu0 ) {it_error ( "Not implemented" );}</span>
70<a name="l00096"></a>00096
[255]71<a name="l00097"></a><a class="code" href="classbdm_1_1BMEF.html#5912dbcf28ae711e30b08c2fa766a3e6">00097</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;};
[210]72<a name="l00098"></a>00098 };
73<a name="l00099"></a>00099
74<a name="l00100"></a>00100 <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt;
[255]75<a name="l00101"></a>00101 <span class="keyword">class </span>mlnorm;
[210]76<a name="l00102"></a>00102
77<a name="l00108"></a>00108 <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt;
[255]78<a name="l00109"></a><a class="code" href="classbdm_1_1enorm.html">00109</a> <span class="keyword">class </span><a class="code" href="classbdm_1_1enorm.html" title="Gaussian density with positive definite (decomposed) covariance matrix.">enorm</a> : <span class="keyword">public</span> <a class="code" href="classbdm_1_1eEF.html" title="General conjugate exponential family posterior density.">eEF</a> {
[210]79<a name="l00110"></a>00110 <span class="keyword">protected</span>:
[255]80<a name="l00112"></a><a class="code" href="classbdm_1_1enorm.html#c702a194720853570d08b65482f842c7">00112</a>         vec <a class="code" href="classbdm_1_1enorm.html#c702a194720853570d08b65482f842c7" title="mean value">mu</a>;
81<a name="l00114"></a><a class="code" href="classbdm_1_1enorm.html#2d92dde696b2a7a5b10ddef5d22ba2c2">00114</a>         sq_T <a class="code" href="classbdm_1_1enorm.html#2d92dde696b2a7a5b10ddef5d22ba2c2" title="Covariance matrix in decomposed form.">R</a>;
82<a name="l00116"></a><a class="code" href="classbdm_1_1enorm.html#91a2d4a91364b0144e1523cad4d1135b">00116</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>;
[210]83<a name="l00117"></a>00117 <span class="keyword">public</span>:
[255]84<a name="l00119"></a>00119         <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> );
85<a name="l00121"></a>00121         <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> );
[210]86<a name="l00123"></a>00123         <span class="comment">//void tupdate ( double phi, mat &amp;vbar, double nubar );</span>
[255]87<a name="l00125"></a>00125 <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 );
[210]88<a name="l00126"></a>00126
[255]89<a name="l00127"></a>00127         vec <a class="code" href="classbdm_1_1enorm.html#e1a48f52351ec3a349bd443b713b1766" title="Returns a sample,  from density .">sample</a>() <span class="keyword">const</span>;
90<a name="l00129"></a>00129         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>;
[219]91<a name="l00130"></a>00130 <span class="comment">//      double eval ( const vec &amp;val ) const ;</span>
[255]92<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>;
93<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>;
94<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>;}
95<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());}
[234]96<a name="l00135"></a>00135 <span class="comment">//      mlnorm&lt;sq_T&gt;* condition ( const RV &amp;rvn ) const ;</span>
[255]97<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> ;
[234]98<a name="l00137"></a>00137 <span class="comment">//      enorm&lt;sq_T&gt;* marginal ( const RV &amp;rv ) const;</span>
[255]99<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="Identified of the random variable.">rv</a> ) <span class="keyword">const</span>;
[234]100<a name="l00139"></a>00139 <span class="comment">//Access methods</span>
[255]101<a name="l00141"></a><a class="code" href="classbdm_1_1enorm.html#766127847e9482aea9226ea157295ea2">00141</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>;}
[234]102<a name="l00142"></a>00142
[255]103<a name="l00144"></a><a class="code" href="classbdm_1_1enorm.html#8915d68ae76ad185c8c314f960a63f0c">00144</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;}
[234]104<a name="l00145"></a>00145
[255]105<a name="l00147"></a><a class="code" href="classbdm_1_1enorm.html#81d81e35e57c9f194bde248e3affcf1f">00147</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>;}
106<a name="l00148"></a>00148         <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>;}
[234]107<a name="l00149"></a>00149
108<a name="l00151"></a>00151 <span class="comment">//      mat getR () {return R.to_mat();}</span>
109<a name="l00152"></a>00152 };
110<a name="l00153"></a>00153
[255]111<a name="l00160"></a><a class="code" href="classbdm_1_1egiw.html">00160</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> {
[234]112<a name="l00161"></a>00161 <span class="keyword">protected</span>:
[255]113<a name="l00163"></a><a class="code" href="classbdm_1_1egiw.html#ae56852845c6af176fd9017dbebbbd52">00163</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>;
114<a name="l00165"></a><a class="code" href="classbdm_1_1egiw.html#447eacf19d4f4083872686f044814dc4">00165</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>;
115<a name="l00167"></a><a class="code" href="classbdm_1_1egiw.html#40b68a9c3b2120fba94cc4d2fcd291e1">00167</a>         <span class="keywordtype">int</span> <a class="code" href="classbdm_1_1egiw.html#40b68a9c3b2120fba94cc4d2fcd291e1" title="Dimension of the output.">xdim</a>;
116<a name="l00169"></a><a class="code" href="classbdm_1_1egiw.html#322414c32d9a21a006a5aab0311f64fd">00169</a>         <span class="keywordtype">int</span> <a class="code" href="classbdm_1_1egiw.html#322414c32d9a21a006a5aab0311f64fd" title="Dimension of the regressor.">nPsi</a>;
[234]117<a name="l00170"></a>00170 <span class="keyword">public</span>:
[255]118<a name="l00172"></a><a class="code" href="classbdm_1_1egiw.html#a60e072c191acf65ab480deeb11c5b88">00172</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 ) {
119<a name="l00173"></a>00173                 <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>();
120<a name="l00174"></a>00174                 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> );
121<a name="l00175"></a>00175                 <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>;
[234]122<a name="l00176"></a>00176                 <span class="comment">//set mu to have proper normalization and </span>
123<a name="l00177"></a>00177                 <span class="keywordflow">if</span> (nu0&lt;0){
[255]124<a name="l00178"></a>00178                         <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>
[234]125<a name="l00179"></a>00179                         <span class="comment">// terms before that are sufficient for finite normalization</span>
126<a name="l00180"></a>00180                 }
127<a name="l00181"></a>00181         }
[255]128<a name="l00183"></a><a class="code" href="classbdm_1_1egiw.html#bc3db93cb60dd29187eb3c6cfd557f97">00183</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 ) {
129<a name="l00184"></a>00184                 <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>();
130<a name="l00185"></a>00185                 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> );
131<a name="l00186"></a>00186                 <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>;
[234]132<a name="l00187"></a>00187                 <span class="keywordflow">if</span> (nu0&lt;0){
[255]133<a name="l00188"></a>00188                         <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>
[234]134<a name="l00189"></a>00189                         <span class="comment">// terms before that are sufficient for finite normalization</span>
135<a name="l00190"></a>00190                 }
136<a name="l00191"></a>00191         }
137<a name="l00192"></a>00192
[255]138<a name="l00193"></a>00193         vec <a class="code" href="classbdm_1_1egiw.html#920f21548b7a3723923dd108fe514c61" title="Returns a sample,  from density .">sample</a>() <span class="keyword">const</span>;
139<a name="l00194"></a>00194         vec <a class="code" href="classbdm_1_1egiw.html#df70c05f918c3a6f86d60f10c1fd6ba2" title="return expected value">mean</a>() <span class="keyword">const</span>;
140<a name="l00195"></a><a class="code" href="classbdm_1_1egiw.html#c1ecc406613cc2341225dc10c3d3b46a">00195</a>         vec <a class="code" href="classbdm_1_1egiw.html#c1ecc406613cc2341225dc10c3d3b46a" title="return expected variance (not covariance!)">variance</a>()<span class="keyword"> const</span>{it_error(<span class="stringliteral">"Not implemented"</span>); <span class="keywordflow">return</span> vec(0);};
[234]141<a name="l00196"></a>00196         <span class="keywordtype">void</span> mean_mat ( mat &amp;M, mat&amp;R ) <span class="keyword">const</span>;
[255]142<a name="l00198"></a>00198         <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>;
143<a name="l00199"></a>00199         <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>;
[234]144<a name="l00200"></a>00200
145<a name="l00201"></a>00201         <span class="comment">//Access</span>
[255]146<a name="l00203"></a><a class="code" href="classbdm_1_1egiw.html#15792f3112e5cf67d572f491b09324c8">00203</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>;}
147<a name="l00205"></a><a class="code" href="classbdm_1_1egiw.html#ad9c539a80a552e837245ddcebcbbba4">00205</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>;}
148<a name="l00207"></a><a class="code" href="classbdm_1_1egiw.html#a025ee710274ca142dd0ae978735ad4a">00207</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>;}
149<a name="l00208"></a>00208         <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>;}
150<a name="l00209"></a><a class="code" href="classbdm_1_1egiw.html#8e610e95401a11baf34f65e16ecd87be">00209</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;};
[234]151<a name="l00210"></a>00210 };
152<a name="l00211"></a>00211
[255]153<a name="l00220"></a><a class="code" href="classbdm_1_1eDirich.html">00220</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> {
[234]154<a name="l00221"></a>00221 <span class="keyword">protected</span>:
[255]155<a name="l00223"></a><a class="code" href="classbdm_1_1eDirich.html#f25886a49b4667af61245de81c83b5d2">00223</a>         vec <a class="code" href="classbdm_1_1eDirich.html#f25886a49b4667af61245de81c83b5d2" title="sufficient statistics">beta</a>;
156<a name="l00225"></a><a class="code" href="classbdm_1_1eDirich.html#ee9db192a6f0ab7b29c33b2a18a5e1b4">00225</a>         <span class="keywordtype">double</span> <a class="code" href="classbdm_1_1eDirich.html#ee9db192a6f0ab7b29c33b2a18a5e1b4" title="speedup variable">gamma</a>;
[234]157<a name="l00226"></a>00226 <span class="keyword">public</span>:
[255]158<a name="l00228"></a><a class="code" href="classbdm_1_1eDirich.html#2ae893fe9167f67bca09bc159acbf957">00228</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> ); };
159<a name="l00230"></a><a class="code" href="classbdm_1_1eDirich.html#31cc8bf709552c9e7286ac16b27c8e2c">00230</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> ) {};
160<a name="l00231"></a><a class="code" href="classbdm_1_1eDirich.html#3290613d31d58daa8a45a54b003871fc">00231</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 );};
161<a name="l00232"></a><a class="code" href="classbdm_1_1eDirich.html#cb343355ec791298bb5a3404cd482fb6">00232</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>;};
162<a name="l00233"></a><a class="code" href="classbdm_1_1eDirich.html#43c547a2507e233706f92712d8c2aacc">00233</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));}
163<a name="l00235"></a><a class="code" href="classbdm_1_1eDirich.html#e09a24938e80c3d94b0ee842d1552318">00235</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>);
[234]164<a name="l00236"></a>00236         <span class="keywordflow">return</span> tmp;};
[255]165<a name="l00237"></a><a class="code" href="classbdm_1_1eDirich.html#279a99f6266c82fe2273e83841f19eb2">00237</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>{
[234]166<a name="l00238"></a>00238                 <span class="keywordtype">double</span> tmp;
[255]167<a name="l00239"></a>00239                 <span class="keywordtype">double</span> gam=sum ( <a class="code" href="classbdm_1_1eDirich.html#f25886a49b4667af61245de81c83b5d2" title="sufficient statistics">beta</a> );
[234]168<a name="l00240"></a>00240                 <span class="keywordtype">double</span> lgb=0.0;
[255]169<a name="l00241"></a>00241                 <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 ) );}
[234]170<a name="l00242"></a>00242                 tmp= lgb-lgamma ( gam );
171<a name="l00243"></a>00243                 it_assert_debug(std::isfinite(tmp),<span class="stringliteral">"Infinite value"</span>);
172<a name="l00244"></a>00244                 <span class="keywordflow">return</span> tmp;
173<a name="l00245"></a>00245         };
[255]174<a name="l00247"></a><a class="code" href="classbdm_1_1eDirich.html#175e0add26d2105c28d8121eefb9e324">00247</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>;}
175<a name="l00249"></a><a class="code" href="classbdm_1_1eDirich.html#a06af2376976a33e1eceaed7e8da75a5">00249</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 ) {
176<a name="l00250"></a>00250                 <span class="keywordflow">if</span> ( beta0.length() !=<a class="code" href="classbdm_1_1eDirich.html#f25886a49b4667af61245de81c83b5d2" title="sufficient statistics">beta</a>.length() ) {
177<a name="l00251"></a>00251                         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> );
178<a name="l00252"></a>00252                         <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() );
[234]179<a name="l00253"></a>00253                 }
[255]180<a name="l00254"></a>00254                 <a class="code" href="classbdm_1_1eDirich.html#f25886a49b4667af61245de81c83b5d2" title="sufficient statistics">beta</a>= beta0;
181<a name="l00255"></a>00255                 <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>);
[234]182<a name="l00256"></a>00256         }
183<a name="l00257"></a>00257 };
184<a name="l00258"></a>00258
[255]185<a name="l00260"></a><a class="code" href="classbdm_1_1multiBM.html">00260</a> <span class="keyword">class </span><a class="code" href="classbdm_1_1multiBM.html" title="Estimator for Multinomial density.">multiBM</a> : <span class="keyword">public</span> <a class="code" href="classbdm_1_1BMEF.html" title="Estimator for Exponential family.">BMEF</a> {
[234]186<a name="l00261"></a>00261 <span class="keyword">protected</span>:
[255]187<a name="l00263"></a><a class="code" href="classbdm_1_1multiBM.html#9ecc6878abbd20eb8d8e43b6ab3f941a">00263</a>         <a class="code" href="classbdm_1_1eDirich.html" title="Dirichlet posterior density.">eDirich</a> <a class="code" href="classbdm_1_1multiBM.html#9ecc6878abbd20eb8d8e43b6ab3f941a" title="Conjugate prior and posterior.">est</a>;
188<a name="l00265"></a><a class="code" href="classbdm_1_1multiBM.html#044263356944c92209eecd39a5187d25">00265</a>         vec &amp;<a class="code" href="classbdm_1_1multiBM.html#044263356944c92209eecd39a5187d25" title="Pointer inside est to sufficient statistics.">beta</a>;
[234]189<a name="l00266"></a>00266 <span class="keyword">public</span>:
[255]190<a name="l00268"></a><a class="code" href="classbdm_1_1multiBM.html#65dc7567b67ce86a8f339dd496ed8e88">00268</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;}}
191<a name="l00270"></a><a class="code" href="classbdm_1_1multiBM.html#c4378cf8037f6bed29c74eea63344b31">00270</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() ) {}
192<a name="l00272"></a><a class="code" href="classbdm_1_1multiBM.html#dbe6b90d410dc062a233d1dc09eeba52">00272</a>         <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1multiBM.html#dbe6b90d410dc062a233d1dc09eeba52" title="Sets sufficient statistics to match that of givefrom mB0.">set_statistics</a> ( <span class="keyword">const</span> <a class="code" href="classbdm_1_1BM.html" title="Bayesian Model of the world, 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>;}
193<a name="l00273"></a><a class="code" href="classbdm_1_1multiBM.html#1e4bf41b61937fd80f34049742e23f95">00273</a>         <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1multiBM.html#1e4bf41b61937fd80f34049742e23f95" title="Incremental Bayes rule.">bayes</a> ( <span class="keyword">const</span> vec &amp;dt ) {
194<a name="l00274"></a>00274                 <span class="keywordflow">if</span> ( <a class="code" href="classbdm_1_1BMEF.html#1331865e10fb1ccef65bb4c47fa3be64" title="forgetting factor">frg</a>&lt;1.0 ) {<a class="code" href="classbdm_1_1multiBM.html#044263356944c92209eecd39a5187d25" title="Pointer inside est to sufficient statistics.">beta</a>*=<a class="code" href="classbdm_1_1BMEF.html#1331865e10fb1ccef65bb4c47fa3be64" title="forgetting factor">frg</a>;<a class="code" href="classbdm_1_1BMEF.html#06e7b3ac03e10017d4288c76888e2865" title="cached value of lognc() in the previous step (used in evaluation of ll )">last_lognc</a>=<a class="code" href="classbdm_1_1multiBM.html#9ecc6878abbd20eb8d8e43b6ab3f941a" title="Conjugate prior and posterior.">est</a>.<a class="code" href="classbdm_1_1eDirich.html#279a99f6266c82fe2273e83841f19eb2" title="logarithm of the normalizing constant, ">lognc</a>();}
195<a name="l00275"></a>00275                 <a class="code" href="classbdm_1_1multiBM.html#044263356944c92209eecd39a5187d25" title="Pointer inside est to sufficient statistics.">beta</a>+=dt;
196<a name="l00276"></a>00276                 <span class="keywordflow">if</span> ( <a class="code" href="classbdm_1_1BM.html#faff0ad12556fe7dc0e2807d4fd938ee" title="If true, the filter will compute likelihood of the data record and store it in ll...">evalll</a> ) {<a class="code" href="classbdm_1_1BM.html#4064b6559d962633e4372b12f4cd204a" title="Logarithm of marginalized data likelihood.">ll</a>=<a class="code" href="classbdm_1_1multiBM.html#9ecc6878abbd20eb8d8e43b6ab3f941a" title="Conjugate prior and posterior.">est</a>.<a class="code" href="classbdm_1_1eDirich.html#279a99f6266c82fe2273e83841f19eb2" title="logarithm of the normalizing constant, ">lognc</a>()-<a class="code" href="classbdm_1_1BMEF.html#06e7b3ac03e10017d4288c76888e2865" title="cached value of lognc() in the previous step (used in evaluation of ll )">last_lognc</a>;}
[234]197<a name="l00277"></a>00277         }
[255]198<a name="l00278"></a><a class="code" href="classbdm_1_1multiBM.html#e157b607c1e3fa91d42aeea44458e2bf">00278</a>         <span class="keywordtype">double</span> <a class="code" href="classbdm_1_1multiBM.html#e157b607c1e3fa91d42aeea44458e2bf">logpred</a> ( <span class="keyword">const</span> vec &amp;dt )<span class="keyword"> const </span>{
199<a name="l00279"></a>00279                 <a class="code" href="classbdm_1_1eDirich.html" title="Dirichlet posterior density.">eDirich</a> pred ( <a class="code" href="classbdm_1_1multiBM.html#9ecc6878abbd20eb8d8e43b6ab3f941a" title="Conjugate prior and posterior.">est</a> );
200<a name="l00280"></a>00280                 vec &amp;<a class="code" href="classbdm_1_1multiBM.html#044263356944c92209eecd39a5187d25" title="Pointer inside est to sufficient statistics.">beta</a> = pred.<a class="code" href="classbdm_1_1eDirich.html#175e0add26d2105c28d8121eefb9e324" title="access function">_beta</a>();
[234]201<a name="l00281"></a>00281
202<a name="l00282"></a>00282                 <span class="keywordtype">double</span> lll;
[255]203<a name="l00283"></a>00283                 <span class="keywordflow">if</span> ( <a class="code" href="classbdm_1_1BMEF.html#1331865e10fb1ccef65bb4c47fa3be64" title="forgetting factor">frg</a>&lt;1.0 )
204<a name="l00284"></a>00284                         {beta*=<a class="code" href="classbdm_1_1BMEF.html#1331865e10fb1ccef65bb4c47fa3be64" title="forgetting factor">frg</a>;lll=pred.<a class="code" href="classbdm_1_1eDirich.html#279a99f6266c82fe2273e83841f19eb2" title="logarithm of the normalizing constant, ">lognc</a>();}
[234]205<a name="l00285"></a>00285                 <span class="keywordflow">else</span>
[255]206<a name="l00286"></a>00286                         <span class="keywordflow">if</span> ( <a class="code" href="classbdm_1_1BM.html#faff0ad12556fe7dc0e2807d4fd938ee" title="If true, the filter will compute likelihood of the data record and store it in ll...">evalll</a> ) {lll=<a class="code" href="classbdm_1_1BMEF.html#06e7b3ac03e10017d4288c76888e2865" title="cached value of lognc() in the previous step (used in evaluation of ll )">last_lognc</a>;}
207<a name="l00287"></a>00287                         <span class="keywordflow">else</span>{lll=pred.<a class="code" href="classbdm_1_1eDirich.html#279a99f6266c82fe2273e83841f19eb2" title="logarithm of the normalizing constant, ">lognc</a>();}
[234]208<a name="l00288"></a>00288
209<a name="l00289"></a>00289                 beta+=dt;
[255]210<a name="l00290"></a>00290                 <span class="keywordflow">return</span> pred.<a class="code" href="classbdm_1_1eDirich.html#279a99f6266c82fe2273e83841f19eb2" title="logarithm of the normalizing constant, ">lognc</a>()-lll;
[234]211<a name="l00291"></a>00291         }
[255]212<a name="l00292"></a><a class="code" href="classbdm_1_1multiBM.html#aaeb18c989088feb8d26d300e4971732">00292</a>         <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1multiBM.html#aaeb18c989088feb8d26d300e4971732" title="Flatten the posterior according to the given BMEF (of the same type!).">flatten</a> ( <span class="keyword">const</span> <a class="code" href="classbdm_1_1BMEF.html" title="Estimator for Exponential family.">BMEF</a>* B ) {
213<a name="l00293"></a>00293                 <span class="keyword">const</span> <a class="code" href="classbdm_1_1multiBM.html" title="Estimator for Multinomial density.">multiBM</a>* E=<span class="keyword">dynamic_cast&lt;</span><span class="keyword">const </span><a class="code" href="classbdm_1_1multiBM.html" title="Estimator for Multinomial density.">multiBM</a>*<span class="keyword">&gt;</span> ( B );
[234]214<a name="l00294"></a>00294                 <span class="comment">// sum(beta) should be equal to sum(B.beta)</span>
[255]215<a name="l00295"></a>00295                 <span class="keyword">const</span> vec &amp;Eb=E-&gt;<a class="code" href="classbdm_1_1multiBM.html#044263356944c92209eecd39a5187d25" title="Pointer inside est to sufficient statistics.">beta</a>;<span class="comment">//const_cast&lt;multiBM*&gt; ( E )-&gt;_beta();</span>
216<a name="l00296"></a>00296                 <a class="code" href="classbdm_1_1multiBM.html#044263356944c92209eecd39a5187d25" title="Pointer inside est to sufficient statistics.">beta</a>*= ( sum ( Eb ) /sum ( <a class="code" href="classbdm_1_1multiBM.html#044263356944c92209eecd39a5187d25" title="Pointer inside est to sufficient statistics.">beta</a> ) );
217<a name="l00297"></a>00297                 <span class="keywordflow">if</span> ( <a class="code" href="classbdm_1_1BM.html#faff0ad12556fe7dc0e2807d4fd938ee" title="If true, the filter will compute likelihood of the data record and store it in ll...">evalll</a> ) {<a class="code" href="classbdm_1_1BMEF.html#06e7b3ac03e10017d4288c76888e2865" title="cached value of lognc() in the previous step (used in evaluation of ll )">last_lognc</a>=<a class="code" href="classbdm_1_1multiBM.html#9ecc6878abbd20eb8d8e43b6ab3f941a" title="Conjugate prior and posterior.">est</a>.<a class="code" href="classbdm_1_1eDirich.html#279a99f6266c82fe2273e83841f19eb2" title="logarithm of the normalizing constant, ">lognc</a>();}
[234]218<a name="l00298"></a>00298         }
[255]219<a name="l00299"></a><a class="code" href="classbdm_1_1multiBM.html#98c22316ecfef589989baca261713c8d">00299</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>;};
220<a name="l00300"></a><a class="code" href="classbdm_1_1multiBM.html#c996f6b9ca930182030e1027318f1ca6">00300</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>;};
[234]221<a name="l00301"></a>00301         <span class="keywordtype">void</span> set_parameters ( <span class="keyword">const</span> vec &amp;beta0 ) {
[255]222<a name="l00302"></a>00302                 <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 );
223<a name="l00303"></a>00303                 <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>();
224<a name="l00304"></a>00304                 <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>();}
[234]225<a name="l00305"></a>00305         }
226<a name="l00306"></a>00306 };
227<a name="l00307"></a>00307
[255]228<a name="l00317"></a><a class="code" href="classbdm_1_1egamma.html">00317</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> {
[234]229<a name="l00318"></a>00318 <span class="keyword">protected</span>:
[255]230<a name="l00320"></a><a class="code" href="classbdm_1_1egamma.html#0901ec983e66b8337aaa506e13b122fa">00320</a>         vec <a class="code" href="classbdm_1_1egamma.html#0901ec983e66b8337aaa506e13b122fa" title="Vector .">alpha</a>;
231<a name="l00322"></a><a class="code" href="classbdm_1_1egamma.html#457bfb1ccb2057df85073e519a15ccc1">00322</a>         vec <a class="code" href="classbdm_1_1egamma.html#457bfb1ccb2057df85073e519a15ccc1" title="Vector .">beta</a>;
[234]232<a name="l00323"></a>00323 <span class="keyword">public</span> :
[255]233<a name="l00325"></a><a class="code" href="classbdm_1_1egamma.html#4dafabaa0881300b18f791bc614ef487">00325</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()) {};
234<a name="l00327"></a><a class="code" href="classbdm_1_1egamma.html#749f82293ff23a8319c1bf52489d2ed2">00327</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;};
235<a name="l00328"></a>00328         vec <a class="code" href="classbdm_1_1egamma.html#6ed82f0fd05f6002487256d8e75a0bbd" title="Returns a sample,  from density .">sample</a>() <span class="keyword">const</span>;
[234]236<a name="l00330"></a>00330 <span class="comment">//      mat sample ( int N ) const;</span>
[255]237<a name="l00331"></a>00331         <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>;
238<a name="l00332"></a>00332         <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>;
239<a name="l00334"></a><a class="code" href="classbdm_1_1egamma.html#498c1fe5e8382ab2f97d629849741855">00334</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>;};
240<a name="l00335"></a><a class="code" href="classbdm_1_1egamma.html#49d256c42cce14c6faa56ec242b57e85">00335</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>);}
241<a name="l00336"></a><a class="code" href="classbdm_1_1egamma.html#36986cc01917cd0796fadc17125bdec1">00336</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>)); }
[234]242<a name="l00337"></a>00337 };
243<a name="l00338"></a>00338
[255]244<a name="l00353"></a><a class="code" href="classbdm_1_1eigamma.html">00353</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> {
[234]245<a name="l00354"></a>00354         <span class="keyword">protected</span>:
[255]246<a name="l00356"></a><a class="code" href="classbdm_1_1eigamma.html#e2c49c77e04a96a9e6a4a628318ceb73">00356</a>                 vec* <a class="code" href="classbdm_1_1eigamma.html#e2c49c77e04a96a9e6a4a628318ceb73" title="Vector .">alpha</a>;
247<a name="l00358"></a><a class="code" href="classbdm_1_1eigamma.html#b2c62f2e869d1304a4055f6a7ac59cde">00358</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>;
248<a name="l00360"></a><a class="code" href="classbdm_1_1eigamma.html#3e5c03201f073033a7db894fa15ddb96">00360</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>;
[234]249<a name="l00361"></a>00361         <span class="keyword">public</span> :
[255]250<a name="l00363"></a><a class="code" href="classbdm_1_1eigamma.html#34a8d2cd08399c3449e2efcda6ea2f89">00363</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>);};
251<a name="l00365"></a><a class="code" href="classbdm_1_1eigamma.html#09e616c95f31acddf7dfef96d1c5d645">00365</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;};
252<a name="l00366"></a><a class="code" href="classbdm_1_1eigamma.html#3aff7bf25ddac27731c60826fcfd878f">00366</a>                 vec <a class="code" href="classbdm_1_1eigamma.html#3aff7bf25ddac27731c60826fcfd878f" title="Returns a sample,  from density .">sample</a>()<span class="keyword"> const </span>{<span class="keywordflow">return</span> 1.0/<a class="code" href="classbdm_1_1eigamma.html#3e5c03201f073033a7db894fa15ddb96" title="internal egamma">eg</a>.<a class="code" href="classbdm_1_1egamma.html#6ed82f0fd05f6002487256d8e75a0bbd" title="Returns a sample,  from density .">sample</a>();};
[234]253<a name="l00368"></a>00368 <span class="comment">//      mat sample ( int N ) const;</span>
[255]254<a name="l00369"></a><a class="code" href="classbdm_1_1eigamma.html#9e26c80c8e6708bfcf2e684958af6f91">00369</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);};
255<a name="l00370"></a><a class="code" href="classbdm_1_1eigamma.html#a52ac6d523e2fe05642d1f50fe66aec2">00370</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>();};
256<a name="l00372"></a><a class="code" href="classbdm_1_1eigamma.html#57b9ee79ef5d2cea243bbe6b274a2abe">00372</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>;};
257<a name="l00373"></a><a class="code" href="classbdm_1_1eigamma.html#46cecb295edbabd28120cb0f6f572bcb">00373</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);}
258<a name="l00374"></a><a class="code" href="classbdm_1_1eigamma.html#c2c696f8c668e9f65392c9449f6a5133">00374</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);}
[234]259<a name="l00375"></a>00375 };
260<a name="l00376"></a>00376 <span class="comment">/*</span>
261<a name="l00378"></a>00378 <span class="comment">class emix : public epdf {</span>
262<a name="l00379"></a>00379 <span class="comment">protected:</span>
263<a name="l00380"></a>00380 <span class="comment">        int n;</span>
264<a name="l00381"></a>00381 <span class="comment">        vec &amp;w;</span>
265<a name="l00382"></a>00382 <span class="comment">        Array&lt;epdf*&gt; Coms;</span>
266<a name="l00383"></a>00383 <span class="comment">public:</span>
267<a name="l00385"></a>00385 <span class="comment">        emix ( const RV &amp;rv, vec &amp;w0): epdf(rv), n(w0.length()), w(w0), Coms(n) {};</span>
268<a name="l00386"></a>00386 <span class="comment">        void set_parameters( int &amp;i, double wi, epdf* ep){w(i)=wi;Coms(i)=ep;}</span>
269<a name="l00387"></a>00387 <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>
270<a name="l00388"></a>00388 <span class="comment">        vec sample() {it_error ( "Not implemented" );return 0;}</span>
271<a name="l00389"></a>00389 <span class="comment">};</span>
272<a name="l00390"></a>00390 <span class="comment">*/</span>
273<a name="l00391"></a>00391
274<a name="l00393"></a>00393
[255]275<a name="l00394"></a><a class="code" href="classbdm_1_1euni.html">00394</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> {
[234]276<a name="l00395"></a>00395 <span class="keyword">protected</span>:
[255]277<a name="l00397"></a><a class="code" href="classbdm_1_1euni.html#ff7ce6a2ef5ef0015bbd1398bed12f32">00397</a>         vec <a class="code" href="classbdm_1_1euni.html#ff7ce6a2ef5ef0015bbd1398bed12f32" title="lower bound on support">low</a>;
278<a name="l00399"></a><a class="code" href="classbdm_1_1euni.html#cfad2dea4a62db6872bda8abd75f0de1">00399</a>         vec <a class="code" href="classbdm_1_1euni.html#cfad2dea4a62db6872bda8abd75f0de1" title="upper bound on support">high</a>;
279<a name="l00401"></a><a class="code" href="classbdm_1_1euni.html#d3c27e331f90c754d80228108de8ed4c">00401</a>         vec <a class="code" href="classbdm_1_1euni.html#d3c27e331f90c754d80228108de8ed4c" title="internal">distance</a>;
280<a name="l00403"></a><a class="code" href="classbdm_1_1euni.html#31bb13e8449a8eff35246d46dae35c20">00403</a>         <span class="keywordtype">double</span> <a class="code" href="classbdm_1_1euni.html#31bb13e8449a8eff35246d46dae35c20" title="normalizing coefficients">nk</a>;
281<a name="l00405"></a><a class="code" href="classbdm_1_1euni.html#3e63be48dd58659663ca60cd18700476">00405</a>         <span class="keywordtype">double</span> <a class="code" href="classbdm_1_1euni.html#3e63be48dd58659663ca60cd18700476" title="cache of log( nk )">lnk</a>;
[234]282<a name="l00406"></a>00406 <span class="keyword">public</span>:
[255]283<a name="l00408"></a><a class="code" href="classbdm_1_1euni.html#dca02eda833d6295e0c19f6e120b64e0">00408</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 ) {}
284<a name="l00409"></a>00409         <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>;}
285<a name="l00410"></a><a class="code" href="classbdm_1_1euni.html#caa07b8307bd793d5339d6583e0aba81">00410</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>;}
286<a name="l00411"></a><a class="code" href="classbdm_1_1euni.html#fc5df80359ead2918384b2004ce67194">00411</a>         vec <a class="code" href="classbdm_1_1euni.html#fc5df80359ead2918384b2004ce67194" title="Returns a sample,  from density .">sample</a>()<span class="keyword"> const </span>{
287<a name="l00412"></a>00412                 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>() );
[234]288<a name="l00413"></a>00413 <span class="preprocessor">#pragma omp critical</span>
[255]289<a name="l00414"></a>00414 <span class="preprocessor"></span>                UniRNG.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 );
290<a name="l00415"></a>00415                 <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 );
[234]291<a name="l00416"></a>00416         }
[255]292<a name="l00418"></a><a class="code" href="classbdm_1_1euni.html#8e130b323c62b42f1699537f99af6f09">00418</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 ) {
293<a name="l00419"></a>00419                 <a class="code" href="classbdm_1_1euni.html#d3c27e331f90c754d80228108de8ed4c" title="internal">distance</a> = high0-low0;
294<a name="l00420"></a>00420                 it_assert_debug ( min ( <a class="code" href="classbdm_1_1euni.html#d3c27e331f90c754d80228108de8ed4c" title="internal">distance</a> ) &gt;0.0,<span class="stringliteral">"bad support"</span> );
295<a name="l00421"></a>00421                 <a class="code" href="classbdm_1_1euni.html#ff7ce6a2ef5ef0015bbd1398bed12f32" title="lower bound on support">low</a> = low0;
296<a name="l00422"></a>00422                 <a class="code" href="classbdm_1_1euni.html#cfad2dea4a62db6872bda8abd75f0de1" title="upper bound on support">high</a> = high0;
297<a name="l00423"></a>00423                 <a class="code" href="classbdm_1_1euni.html#31bb13e8449a8eff35246d46dae35c20" title="normalizing coefficients">nk</a> = prod ( 1.0/<a class="code" href="classbdm_1_1euni.html#d3c27e331f90c754d80228108de8ed4c" title="internal">distance</a> );
298<a name="l00424"></a>00424                 <a class="code" href="classbdm_1_1euni.html#3e63be48dd58659663ca60cd18700476" title="cache of log( nk )">lnk</a> = log ( <a class="code" href="classbdm_1_1euni.html#31bb13e8449a8eff35246d46dae35c20" title="normalizing coefficients">nk</a> );
[234]299<a name="l00425"></a>00425         }
[255]300<a name="l00426"></a><a class="code" href="classbdm_1_1euni.html#46caa8c13aba2e6228f964208918b226">00426</a>         vec <a class="code" href="classbdm_1_1euni.html#46caa8c13aba2e6228f964208918b226" title="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;}
301<a name="l00427"></a><a class="code" href="classbdm_1_1euni.html#951f932155111f6053c980f672b4c22c">00427</a>         vec <a class="code" href="classbdm_1_1euni.html#951f932155111f6053c980f672b4c22c" title="return expected variance (not covariance!)">variance</a>()<span class="keyword"> const </span>{<span class="keywordflow">return</span> (pow(<a class="code" href="classbdm_1_1euni.html#cfad2dea4a62db6872bda8abd75f0de1" title="upper bound on support">high</a>,2)+pow(<a class="code" href="classbdm_1_1euni.html#ff7ce6a2ef5ef0015bbd1398bed12f32" title="lower bound on support">low</a>,2)+elem_mult(<a class="code" href="classbdm_1_1euni.html#cfad2dea4a62db6872bda8abd75f0de1" title="upper bound on support">high</a>,<a class="code" href="classbdm_1_1euni.html#ff7ce6a2ef5ef0015bbd1398bed12f32" title="lower bound on support">low</a>))/3.0;}
[234]302<a name="l00428"></a>00428 };
303<a name="l00429"></a>00429
304<a name="l00430"></a>00430
305<a name="l00436"></a>00436 <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt;
[255]306<a name="l00437"></a><a class="code" href="classbdm_1_1mlnorm.html">00437</a> <span class="keyword">class </span><a class="code" href="classbdm_1_1mlnorm.html" title="Normal distributed linear function with linear function of mean value;.">mlnorm</a> : <span class="keyword">public</span> <a class="code" href="classbdm_1_1mEF.html" title="Exponential family model.">mEF</a> {
[234]307<a name="l00438"></a>00438 <span class="keyword">protected</span>:
[255]308<a name="l00440"></a><a class="code" href="classbdm_1_1mlnorm.html#150ad6acb223b0a0abeaf92346686dcd">00440</a>         <a class="code" href="classbdm_1_1enorm.html" title="Gaussian density with positive definite (decomposed) covariance matrix.">enorm&lt;sq_T&gt;</a> <a class="code" href="classbdm_1_1epdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a>;
[234]309<a name="l00441"></a>00441         mat A;
310<a name="l00442"></a>00442         vec mu_const;
311<a name="l00443"></a>00443         vec&amp; _mu; <span class="comment">//cached epdf.mu;</span>
312<a name="l00444"></a>00444 <span class="keyword">public</span>:
[255]313<a name="l00446"></a>00446         <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> );
314<a name="l00448"></a>00448         <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 );
[234]315<a name="l00449"></a>00449 <span class="comment">//      //!Generate one sample of the posterior</span>
316<a name="l00450"></a>00450 <span class="comment">//      vec samplecond (const vec &amp;cond, double &amp;lik );</span>
317<a name="l00451"></a>00451 <span class="comment">//      //!Generate matrix of samples of the posterior</span>
318<a name="l00452"></a>00452 <span class="comment">//      mat samplecond (const vec &amp;cond, vec &amp;lik, int n );</span>
[255]319<a name="l00454"></a>00454 <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 );
[234]320<a name="l00455"></a>00455
[255]321<a name="l00457"></a><a class="code" href="classbdm_1_1mlnorm.html#56e587952f94fcac6cfc999eae6dbced">00457</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;}
322<a name="l00459"></a><a class="code" href="classbdm_1_1mlnorm.html#262a2a486bff585f34bb6a5005b02614">00459</a>         mat&amp; <a class="code" href="classbdm_1_1mlnorm.html#262a2a486bff585f34bb6a5005b02614" title="access function">_A</a>() {<span class="keywordflow">return</span> A;}
323<a name="l00461"></a><a class="code" href="classbdm_1_1mlnorm.html#78120ecd1c2b1d7e80124b4603504604">00461</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();}
[234]324<a name="l00462"></a>00462
325<a name="l00463"></a>00463         <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_M&gt;
326<a name="l00464"></a>00464         <span class="keyword">friend</span> std::ostream &amp;operator&lt;&lt; ( std::ostream &amp;os,  mlnorm&lt;sq_M&gt; &amp;ml );
327<a name="l00465"></a>00465 };
328<a name="l00466"></a>00466
[255]329<a name="l00469"></a><a class="code" href="classbdm_1_1mlstudent.html">00469</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; {
[234]330<a name="l00470"></a>00470 <span class="keyword">protected</span>:
331<a name="l00471"></a>00471         <a class="code" href="classldmat.html" title="Matrix stored in LD form, (commonly known as UD).">ldmat</a> Lambda;
[255]332<a name="l00472"></a>00472         <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>;
[234]333<a name="l00473"></a>00473         <a class="code" href="classldmat.html" title="Matrix stored in LD form, (commonly known as UD).">ldmat</a> Re;
334<a name="l00474"></a>00474 <span class="keyword">public</span>:
[255]335<a name="l00475"></a>00475         <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 ),
336<a name="l00476"></a>00476                         Lambda ( rv0.<a class="code" href="classbdm_1_1RV.html#2174751a00ce19f941edd2c1a861be67" title="Return number of scalars in the RV.">count</a>() ),
337<a name="l00477"></a>00477                         <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() ) {}
[234]338<a name="l00478"></a>00478         <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) {
[255]339<a name="l00479"></a>00479                 <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 );
[234]340<a name="l00480"></a>00480                 A = A0;
341<a name="l00481"></a>00481                 mu_const = mu0;
342<a name="l00482"></a>00482                 Re=R0;
343<a name="l00483"></a>00483                 Lambda = Lambda0;
344<a name="l00484"></a>00484         }
[255]345<a name="l00485"></a><a class="code" href="classbdm_1_1mlstudent.html#efd37560585c8613897f30d3c2f58d0d">00485</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 ) {
[234]346<a name="l00486"></a>00486                 _mu = A*cond + mu_const;
347<a name="l00487"></a>00487                 <span class="keywordtype">double</span> zeta;
348<a name="l00488"></a>00488                 <span class="comment">//ugly hack!</span>
349<a name="l00489"></a>00489                 <span class="keywordflow">if</span> ((cond.length()+1)==Lambda.<a class="code" href="group__math.html#g96dfb21865db4f5bd36fa70f9b0b1163" title="access function">rows</a>()){
[255]350<a name="l00490"></a>00490                         zeta = Lambda.<a class="code" href="classldmat.html#d876c5f83e02b3e809b35c9de5068f14" title="Evaluates quadratic form ;.">invqform</a> ( concat(cond, vec_1(1.0)) );
[234]351<a name="l00491"></a>00491                 } <span class="keywordflow">else</span> {
352<a name="l00492"></a>00492                         zeta = Lambda.<a class="code" href="classldmat.html#d876c5f83e02b3e809b35c9de5068f14" title="Evaluates quadratic form ;.">invqform</a> ( cond );
353<a name="l00493"></a>00493                 }
[255]354<a name="l00494"></a>00494                 <a class="code" href="classbdm_1_1mlnorm.html#78120ecd1c2b1d7e80124b4603504604" title="access function">_R</a> = Re;
355<a name="l00495"></a>00495                 <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>
[234]356<a name="l00496"></a>00496         };
357<a name="l00497"></a>00497
358<a name="l00498"></a>00498 };
[255]359<a name="l00508"></a><a class="code" href="classbdm_1_1mgamma.html">00508</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> {
[234]360<a name="l00509"></a>00509 <span class="keyword">protected</span>:
[255]361<a name="l00511"></a><a class="code" href="classbdm_1_1mgamma.html#bdc9f1e9e03c09e91103fee269864438">00511</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>;
362<a name="l00513"></a><a class="code" href="classbdm_1_1mgamma.html#b20cf88cca1fe9b0b8f2a412608bfd09">00513</a>         <span class="keywordtype">double</span> <a class="code" href="classbdm_1_1mgamma.html#b20cf88cca1fe9b0b8f2a412608bfd09" title="Constant .">k</a>;
363<a name="l00515"></a><a class="code" href="classbdm_1_1mgamma.html#f6a652ce70fa2eb4d2c7ba6d5a6ae343">00515</a>         vec* <a class="code" href="classbdm_1_1mgamma.html#f6a652ce70fa2eb4d2c7ba6d5a6ae343" title="cache of epdf.beta">_beta</a>;
[234]364<a name="l00516"></a>00516
365<a name="l00517"></a>00517 <span class="keyword">public</span>:
[255]366<a name="l00519"></a><a class="code" href="classbdm_1_1mgamma.html#2f6425cd966191b0be4c6ea91a40b6d9">00519</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>;};
367<a name="l00521"></a>00521         <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> );
368<a name="l00522"></a><a class="code" href="classbdm_1_1mgamma.html#8996500f1885e39cde30221b20900bff">00522</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;};
[234]369<a name="l00523"></a>00523 };
370<a name="l00524"></a>00524
[255]371<a name="l00534"></a><a class="code" href="classbdm_1_1migamma.html">00534</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> {
[234]372<a name="l00535"></a>00535         <span class="keyword">protected</span>:
[255]373<a name="l00537"></a><a class="code" href="classbdm_1_1migamma.html#a31b39d4179551b593c9e0d7d756783a">00537</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>;
374<a name="l00539"></a><a class="code" href="classbdm_1_1migamma.html#dc56bc9da542e0103ec16b9be8e5e38c">00539</a>                 <span class="keywordtype">double</span> <a class="code" href="classbdm_1_1migamma.html#dc56bc9da542e0103ec16b9be8e5e38c" title="Constant .">k</a>;
375<a name="l00541"></a><a class="code" href="classbdm_1_1migamma.html#4825c0ef11a148bad9b802a496f56f96">00541</a>                 vec* <a class="code" href="classbdm_1_1migamma.html#4825c0ef11a148bad9b802a496f56f96" title="cache of epdf.beta">_beta</a>;
376<a name="l00543"></a><a class="code" href="classbdm_1_1migamma.html#b6c265b132ff79963bf51dff4c3ef252">00543</a>                 vec* <a class="code" href="classbdm_1_1migamma.html#b6c265b132ff79963bf51dff4c3ef252" title="chaceh of epdf.alpha">_alpha</a>;
[234]377<a name="l00544"></a>00544
378<a name="l00545"></a>00545         <span class="keyword">public</span>:
[255]379<a name="l00547"></a><a class="code" href="classbdm_1_1migamma.html#07c5970da0e578ce8a428f1ebf46a459">00547</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>;};
380<a name="l00549"></a><a class="code" href="classbdm_1_1migamma.html#1d7023b1565551d0260eb1ba832bebaf">00549</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;};
381<a name="l00550"></a><a class="code" href="classbdm_1_1migamma.html#7a34b1e2e3aa2250d7c0ed7df1665b8c">00550</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 ) {
382<a name="l00551"></a>00551                         *<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));
[234]383<a name="l00552"></a>00552                 };
384<a name="l00553"></a>00553 };
385<a name="l00554"></a>00554
[255]386<a name="l00566"></a><a class="code" href="classbdm_1_1mgamma__fix.html">00566</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> {
[234]387<a name="l00567"></a>00567 <span class="keyword">protected</span>:
[255]388<a name="l00569"></a><a class="code" href="classbdm_1_1mgamma__fix.html#1eb701506aabb2e6af007e487212d6fa">00569</a>         <span class="keywordtype">double</span> <a class="code" href="classbdm_1_1mgamma__fix.html#1eb701506aabb2e6af007e487212d6fa" title="parameter l">l</a>;
389<a name="l00571"></a><a class="code" href="classbdm_1_1mgamma__fix.html#018c6f901a04e419455308a07eb3b0b2">00571</a>         vec <a class="code" href="classbdm_1_1mgamma__fix.html#018c6f901a04e419455308a07eb3b0b2" title="reference vector">refl</a>;
[234]390<a name="l00572"></a>00572 <span class="keyword">public</span>:
[255]391<a name="l00574"></a><a class="code" href="classbdm_1_1mgamma__fix.html#c73571f45ab2926e5a7fb9c3791b5614">00574</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() ) {};
392<a name="l00576"></a><a class="code" href="classbdm_1_1mgamma__fix.html#1bfd30e90db9dc1fbda4a9fbb0b716b2">00576</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 ) {
393<a name="l00577"></a>00577                 <a class="code" href="classbdm_1_1mgamma.html#0b486f7e52a3d8a39adbcbd325461c0d" title="Set value of k.">mgamma::set_parameters</a> ( k0 );
394<a name="l00578"></a>00578                 <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;
[234]395<a name="l00579"></a>00579         };
396<a name="l00580"></a>00580
[255]397<a name="l00581"></a><a class="code" href="classbdm_1_1mgamma__fix.html#1d539591deb7a38bb3403c2b396c8ff7">00581</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;};
[234]398<a name="l00582"></a>00582 };
399<a name="l00583"></a>00583
400<a name="l00584"></a>00584
[255]401<a name="l00597"></a><a class="code" href="classbdm_1_1migamma__fix.html">00597</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> {
[234]402<a name="l00598"></a>00598         <span class="keyword">protected</span>:
[255]403<a name="l00600"></a><a class="code" href="classbdm_1_1migamma__fix.html#e1c344accac36d7ccc3ffa502e8d2f4e">00600</a>                 <span class="keywordtype">double</span> <a class="code" href="classbdm_1_1migamma__fix.html#e1c344accac36d7ccc3ffa502e8d2f4e" title="parameter l">l</a>;
404<a name="l00602"></a><a class="code" href="classbdm_1_1migamma__fix.html#5d453e5a2bdfc9a16c8acb8842dc9780">00602</a>                 vec <a class="code" href="classbdm_1_1migamma__fix.html#5d453e5a2bdfc9a16c8acb8842dc9780" title="reference vector">refl</a>;
[234]405<a name="l00603"></a>00603         <span class="keyword">public</span>:
[255]406<a name="l00605"></a><a class="code" href="classbdm_1_1migamma__fix.html#3c6aacebccbe6d73f8d442e82d3cb53a">00605</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() ) {};
407<a name="l00607"></a><a class="code" href="classbdm_1_1migamma__fix.html#17f9ce1068660a4e8c05173bef7d7440">00607</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 ) {
408<a name="l00608"></a>00608                         <a class="code" href="classbdm_1_1migamma.html#1d7023b1565551d0260eb1ba832bebaf" title="Set value of k.">migamma::set_parameters</a> ( k0 );
409<a name="l00609"></a>00609                         <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;
[234]410<a name="l00610"></a>00610                 };
411<a name="l00611"></a>00611
[255]412<a name="l00612"></a><a class="code" href="classbdm_1_1migamma__fix.html#cfbabd293795d44aae1b7c874e57c4b8">00612</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);};
[234]413<a name="l00613"></a>00613 };
[255]414<a name="l00615"></a>00615 <span class="keyword">enum</span> RESAMPLING_METHOD { MULTINOMIAL = 0, STRATIFIED = 1, SYSTEMATIC = 3 };
415<a name="l00621"></a><a class="code" href="classbdm_1_1eEmp.html">00621</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> {
[234]416<a name="l00622"></a>00622 <span class="keyword">protected</span> :
[255]417<a name="l00624"></a><a class="code" href="classbdm_1_1eEmp.html#9798006271ca77629855113f1283a031">00624</a>         <span class="keywordtype">int</span> <a class="code" href="classbdm_1_1eEmp.html#9798006271ca77629855113f1283a031" title="Number of particles.">n</a>;
418<a name="l00626"></a><a class="code" href="classbdm_1_1eEmp.html#9d39241aab7c4bbeb07c6d516421c67d">00626</a>         vec <a class="code" href="classbdm_1_1eEmp.html#9d39241aab7c4bbeb07c6d516421c67d" title="Sample weights .">w</a>;
419<a name="l00628"></a><a class="code" href="classbdm_1_1eEmp.html#73d819553a0f268b055a087d2d4486f3">00628</a>         Array&lt;vec&gt; <a class="code" href="classbdm_1_1eEmp.html#73d819553a0f268b055a087d2d4486f3" title="Samples .">samples</a>;
[234]420<a name="l00629"></a>00629 <span class="keyword">public</span>:
[255]421<a name="l00631"></a><a class="code" href="classbdm_1_1eEmp.html#47ee4feee19b3f3e2d371f8fc9f9a863">00631</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> ) {};
422<a name="l00633"></a>00633         <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 );
423<a name="l00635"></a>00635         <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 );
424<a name="l00637"></a><a class="code" href="classbdm_1_1eEmp.html#dccd02eaa4c45e858a6351723686ac85">00637</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);};
425<a name="l00639"></a><a class="code" href="classbdm_1_1eEmp.html#d7f83cc0415cd44ae7cc8b4bdad93aef">00639</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>;};
426<a name="l00641"></a><a class="code" href="classbdm_1_1eEmp.html#b7d7106f486e3fad38590914a693d714">00641</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>;};
427<a name="l00643"></a><a class="code" href="classbdm_1_1eEmp.html#c24966b0aaeb767bc8a6b4fd60931be2">00643</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>;};
428<a name="l00645"></a><a class="code" href="classbdm_1_1eEmp.html#b59af0efdb009d98ea8ebfa965e74ae2">00645</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>;};
429<a name="l00647"></a>00647         ivec <a class="code" href="classbdm_1_1eEmp.html#f06ce255de5dbb2313f52ee51f82ba3d" title="Function performs resampling, i.e. removal of low-weight samples and duplication...">resample</a> ( RESAMPLING_METHOD method = SYSTEMATIC );
430<a name="l00649"></a><a class="code" href="classbdm_1_1eEmp.html#97f1e07b5ae6eebc91c7365f0f88d270">00649</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;}
431<a name="l00651"></a><a class="code" href="classbdm_1_1eEmp.html#01654c014d3aa068f8d4ecba4be86d09">00651</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;}
432<a name="l00652"></a><a class="code" href="classbdm_1_1eEmp.html#bbfcb4f868c7381298c281a256d8c4b9">00652</a>         vec <a class="code" href="classbdm_1_1eEmp.html#bbfcb4f868c7381298c281a256d8c4b9" title="return expected value">mean</a>()<span class="keyword"> const </span>{
433<a name="l00653"></a>00653                 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>() );
434<a name="l00654"></a>00654                 <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 );}
[234]435<a name="l00655"></a>00655                 <span class="keywordflow">return</span> pom;
436<a name="l00656"></a>00656         }
[255]437<a name="l00657"></a><a class="code" href="classbdm_1_1eEmp.html#05e9ebf467ede737cb6a3621d7fd3c87">00657</a>         vec <a class="code" href="classbdm_1_1eEmp.html#05e9ebf467ede737cb6a3621d7fd3c87" title="return expected variance (not covariance!)">variance</a>()<span class="keyword"> const </span>{
438<a name="l00658"></a>00658                 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>() );
439<a name="l00659"></a>00659                 <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 );}
440<a name="l00660"></a>00660                 <span class="keywordflow">return</span> pom-pow(<a class="code" href="classbdm_1_1eEmp.html#bbfcb4f868c7381298c281a256d8c4b9" title="return expected value">mean</a>(),2);
[234]441<a name="l00661"></a>00661         }
442<a name="l00662"></a>00662 };
443<a name="l00663"></a>00663
444<a name="l00664"></a>00664
445<a name="l00666"></a>00666
446<a name="l00667"></a>00667 <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt;
[255]447<a name="l00668"></a><a class="code" href="classbdm_1_1enorm.html#7d433390d6bbad337986945b63d7fbe9">00668</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() ) {};
[234]448<a name="l00669"></a>00669
449<a name="l00670"></a>00670 <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt;
[255]450<a name="l00671"></a><a class="code" href="classbdm_1_1enorm.html#b8322f2c11560871dd922c660f4771bb">00671</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 ) {
[234]451<a name="l00672"></a>00672 <span class="comment">//Fixme test dimensions of mu0 and R0;</span>
[255]452<a name="l00673"></a>00673         <a class="code" href="classbdm_1_1enorm.html#c702a194720853570d08b65482f842c7" title="mean value">mu</a> = mu0;
453<a name="l00674"></a>00674         <a class="code" href="classbdm_1_1enorm.html#2d92dde696b2a7a5b10ddef5d22ba2c2" title="Covariance matrix in decomposed form.">R</a> = R0;
[234]454<a name="l00675"></a>00675 };
455<a name="l00676"></a>00676
456<a name="l00677"></a>00677 <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt;
[255]457<a name="l00678"></a><a class="code" href="classbdm_1_1enorm.html#d2e0d3a1e30ab3ab04df2d0c43ae74a2">00678</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 ) {
[234]458<a name="l00679"></a>00679         <span class="comment">//</span>
459<a name="l00680"></a>00680 };
460<a name="l00681"></a>00681
461<a name="l00682"></a>00682 <span class="comment">// template&lt;class sq_T&gt;</span>
462<a name="l00683"></a>00683 <span class="comment">// void enorm&lt;sq_T&gt;::tupdate ( double phi, mat &amp;vbar, double nubar ) {</span>
463<a name="l00684"></a>00684 <span class="comment">//      //</span>
464<a name="l00685"></a>00685 <span class="comment">// };</span>
465<a name="l00686"></a>00686
466<a name="l00687"></a>00687 <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt;
[255]467<a name="l00688"></a><a class="code" href="classbdm_1_1enorm.html#e1a48f52351ec3a349bd443b713b1766">00688</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>{
468<a name="l00689"></a>00689         vec x ( <a class="code" href="classbdm_1_1enorm.html#91a2d4a91364b0144e1523cad4d1135b" title="dimension (redundant from rv.count() for easier coding )">dim</a> );
469<a name="l00690"></a>00690 <span class="preprocessor">        #pragma omp critical </span>
470<a name="l00691"></a>00691 <span class="preprocessor"></span>        NorRNG.sample_vector ( <a class="code" href="classbdm_1_1enorm.html#91a2d4a91364b0144e1523cad4d1135b" title="dimension (redundant from rv.count() for easier coding )">dim</a>,x );
471<a name="l00692"></a>00692         vec smp = <a class="code" href="classbdm_1_1enorm.html#2d92dde696b2a7a5b10ddef5d22ba2c2" title="Covariance matrix in decomposed form.">R</a>.sqrt_mult ( x );
472<a name="l00693"></a>00693
473<a name="l00694"></a>00694         smp += <a class="code" href="classbdm_1_1enorm.html#c702a194720853570d08b65482f842c7" title="mean value">mu</a>;
474<a name="l00695"></a>00695         <span class="keywordflow">return</span> smp;
475<a name="l00696"></a>00696 };
476<a name="l00697"></a>00697
477<a name="l00698"></a>00698 <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt;
478<a name="l00699"></a><a class="code" href="classbdm_1_1enorm.html#ebd96125aed74f9504033bb3605849db">00699</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>{
479<a name="l00700"></a>00700         mat X ( <a class="code" href="classbdm_1_1enorm.html#91a2d4a91364b0144e1523cad4d1135b" title="dimension (redundant from rv.count() for easier coding )">dim</a>,N );
480<a name="l00701"></a>00701         vec x ( <a class="code" href="classbdm_1_1enorm.html#91a2d4a91364b0144e1523cad4d1135b" title="dimension (redundant from rv.count() for easier coding )">dim</a> );
481<a name="l00702"></a>00702         vec pom;
482<a name="l00703"></a>00703         <span class="keywordtype">int</span> i;
483<a name="l00704"></a>00704
484<a name="l00705"></a>00705         <span class="keywordflow">for</span> ( i=0;i&lt;N;i++ ) {
485<a name="l00706"></a>00706 <span class="preprocessor">        #pragma omp critical </span>
486<a name="l00707"></a>00707 <span class="preprocessor"></span>                NorRNG.sample_vector ( <a class="code" href="classbdm_1_1enorm.html#91a2d4a91364b0144e1523cad4d1135b" title="dimension (redundant from rv.count() for easier coding )">dim</a>,x );
487<a name="l00708"></a>00708                 pom = <a class="code" href="classbdm_1_1enorm.html#2d92dde696b2a7a5b10ddef5d22ba2c2" title="Covariance matrix in decomposed form.">R</a>.sqrt_mult ( x );
488<a name="l00709"></a>00709                 pom +=<a class="code" href="classbdm_1_1enorm.html#c702a194720853570d08b65482f842c7" title="mean value">mu</a>;
489<a name="l00710"></a>00710                 X.set_col ( i, pom );
490<a name="l00711"></a>00711         }
491<a name="l00712"></a>00712
492<a name="l00713"></a>00713         <span class="keywordflow">return</span> X;
493<a name="l00714"></a>00714 };
494<a name="l00715"></a>00715
495<a name="l00716"></a>00716 <span class="comment">// template&lt;class sq_T&gt;</span>
496<a name="l00717"></a>00717 <span class="comment">// double enorm&lt;sq_T&gt;::eval ( const vec &amp;val ) const {</span>
497<a name="l00718"></a>00718 <span class="comment">//      double pdfl,e;</span>
498<a name="l00719"></a>00719 <span class="comment">//      pdfl = evallog ( val );</span>
499<a name="l00720"></a>00720 <span class="comment">//      e = exp ( pdfl );</span>
500<a name="l00721"></a>00721 <span class="comment">//      return e;</span>
501<a name="l00722"></a>00722 <span class="comment">// };</span>
502<a name="l00723"></a>00723
503<a name="l00724"></a>00724 <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt;
504<a name="l00725"></a><a class="code" href="classbdm_1_1enorm.html#e13aeed5b543b2179bacdc4fa2ae47a3">00725</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>{
505<a name="l00726"></a>00726         <span class="comment">// 1.83787706640935 = log(2pi)</span>
506<a name="l00727"></a>00727         <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>
507<a name="l00728"></a>00728         <span class="keywordflow">return</span>  tmp;
508<a name="l00729"></a>00729 };
509<a name="l00730"></a>00730
510<a name="l00731"></a>00731 <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt;
511<a name="l00732"></a><a class="code" href="classbdm_1_1enorm.html#25785343aff102cc5df1cab08ba16d32">00732</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>{
512<a name="l00733"></a>00733         <span class="comment">// 1.83787706640935 = log(2pi)</span>
513<a name="l00734"></a>00734         <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() );
514<a name="l00735"></a>00735         <span class="keywordflow">return</span> tmp;
515<a name="l00736"></a>00736 };
516<a name="l00737"></a>00737
517<a name="l00738"></a>00738 <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt;
518<a name="l00739"></a><a class="code" href="classbdm_1_1mlnorm.html#64d965df6811ff65b94718c427048f4a">00739</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>() ) {
519<a name="l00740"></a>00740         <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>;
520<a name="l00741"></a>00741 }
521<a name="l00742"></a>00742
522<a name="l00743"></a>00743 <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt;
523<a name="l00744"></a><a class="code" href="classbdm_1_1mlnorm.html#5d18dec3167584338a4775c1d165d96f">00744</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 ) {
524<a name="l00745"></a>00745         <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 );
525<a name="l00746"></a>00746         A = A0;
526<a name="l00747"></a>00747         mu_const = mu0;
527<a name="l00748"></a>00748 }
528<a name="l00749"></a>00749
529<a name="l00750"></a>00750 <span class="comment">// template&lt;class sq_T&gt;</span>
530<a name="l00751"></a>00751 <span class="comment">// vec mlnorm&lt;sq_T&gt;::samplecond (const  vec &amp;cond, double &amp;lik ) {</span>
531<a name="l00752"></a>00752 <span class="comment">//      this-&gt;condition ( cond );</span>
532<a name="l00753"></a>00753 <span class="comment">//      vec smp = epdf.sample();</span>
533<a name="l00754"></a>00754 <span class="comment">//      lik = epdf.eval ( smp );</span>
534<a name="l00755"></a>00755 <span class="comment">//      return smp;</span>
535<a name="l00756"></a>00756 <span class="comment">// }</span>
536<a name="l00757"></a>00757
537<a name="l00758"></a>00758 <span class="comment">// template&lt;class sq_T&gt;</span>
538<a name="l00759"></a>00759 <span class="comment">// mat mlnorm&lt;sq_T&gt;::samplecond (const vec &amp;cond, vec &amp;lik, int n ) {</span>
539<a name="l00760"></a>00760 <span class="comment">//      int i;</span>
540<a name="l00761"></a>00761 <span class="comment">//      int dim = rv.count();</span>
541<a name="l00762"></a>00762 <span class="comment">//      mat Smp ( dim,n );</span>
542<a name="l00763"></a>00763 <span class="comment">//      vec smp ( dim );</span>
543<a name="l00764"></a>00764 <span class="comment">//      this-&gt;condition ( cond );</span>
544<a name="l00765"></a>00765 <span class="comment">//</span>
545<a name="l00766"></a>00766 <span class="comment">//      for ( i=0; i&lt;n; i++ ) {</span>
546<a name="l00767"></a>00767 <span class="comment">//              smp = epdf.sample();</span>
547<a name="l00768"></a>00768 <span class="comment">//              lik ( i ) = epdf.eval ( smp );</span>
548<a name="l00769"></a>00769 <span class="comment">//              Smp.set_col ( i ,smp );</span>
549<a name="l00770"></a>00770 <span class="comment">//      }</span>
550<a name="l00771"></a>00771 <span class="comment">//</span>
551<a name="l00772"></a>00772 <span class="comment">//      return Smp;</span>
552<a name="l00773"></a>00773 <span class="comment">// }</span>
553<a name="l00774"></a>00774
554<a name="l00775"></a>00775 <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt;
555<a name="l00776"></a><a class="code" href="classbdm_1_1mlnorm.html#0dafc0196e7e07fd06dc6716e0e18bc2">00776</a> <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1mlnorm.html" title="Normal distributed linear function with linear function of mean value;.">mlnorm&lt;sq_T&gt;::condition</a> ( <span class="keyword">const</span> vec &amp;cond ) {
556<a name="l00777"></a>00777         _mu = A*cond + mu_const;
557<a name="l00778"></a>00778 <span class="comment">//R is already assigned;</span>
558<a name="l00779"></a>00779 }
559<a name="l00780"></a>00780
560<a name="l00781"></a>00781 <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt;
561<a name="l00782"></a><a class="code" href="classbdm_1_1enorm.html#cd02d76e9d4f96bdd3fa6b604e273039">00782</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>{
562<a name="l00783"></a>00783         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> );
563<a name="l00784"></a>00784
564<a name="l00785"></a>00785         sq_T Rn ( <a class="code" href="classbdm_1_1enorm.html#2d92dde696b2a7a5b10ddef5d22ba2c2" title="Covariance matrix in decomposed form.">R</a>,irvn );
565<a name="l00786"></a>00786         <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 );
566<a name="l00787"></a>00787         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 );
567<a name="l00788"></a>00788         <span class="keywordflow">return</span> tmp;
568<a name="l00789"></a>00789 }
569<a name="l00790"></a>00790
570<a name="l00791"></a>00791 <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt;
571<a name="l00792"></a><a class="code" href="classbdm_1_1enorm.html#baea4d49c657342b58297d68cda16d26">00792</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>{
572<a name="l00793"></a>00793
573<a name="l00794"></a>00794         <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 );
574<a name="l00795"></a>00795         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> );
575<a name="l00796"></a>00796         <span class="comment">//Permutation vector of the new R</span>
576<a name="l00797"></a>00797         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> );
577<a name="l00798"></a>00798         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> );
578<a name="l00799"></a>00799         ivec perm=concat ( irvn , irvc );
579<a name="l00800"></a>00800         sq_T Rn ( <a class="code" href="classbdm_1_1enorm.html#2d92dde696b2a7a5b10ddef5d22ba2c2" title="Covariance matrix in decomposed form.">R</a>,perm );
580<a name="l00801"></a>00801
581<a name="l00802"></a>00802         <span class="comment">//fixme - could this be done in general for all sq_T?</span>
582<a name="l00803"></a>00803         mat S=Rn.to_mat();
583<a name="l00804"></a>00804         <span class="comment">//fixme</span>
584<a name="l00805"></a>00805         <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;
585<a name="l00806"></a>00806         <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;
586<a name="l00807"></a>00807         mat S11 = S.get ( 0,n, 0, n );
587<a name="l00808"></a>00808         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 );
588<a name="l00809"></a>00809         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 );
589<a name="l00810"></a>00810
590<a name="l00811"></a>00811         vec mu1 = <a class="code" href="classbdm_1_1enorm.html#c702a194720853570d08b65482f842c7" title="mean value">mu</a> ( irvn );
591<a name="l00812"></a>00812         vec mu2 = <a class="code" href="classbdm_1_1enorm.html#c702a194720853570d08b65482f842c7" title="mean value">mu</a> ( irvc );
592<a name="l00813"></a>00813         mat A=S12*inv ( S22 );
593<a name="l00814"></a>00814         sq_T R_n ( S11 - A *S12.T() );
[234]594<a name="l00815"></a>00815
[255]595<a name="l00816"></a>00816         <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 );
596<a name="l00817"></a>00817
597<a name="l00818"></a>00818         tmp-&gt;set_parameters ( A,mu1-A*mu2,R_n );
598<a name="l00819"></a>00819         <span class="keywordflow">return</span> tmp;
599<a name="l00820"></a>00820 }
[234]600<a name="l00821"></a>00821
[255]601<a name="l00823"></a>00823
602<a name="l00824"></a>00824 <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt;
603<a name="l00825"></a>00825 std::ostream &amp;operator&lt;&lt; ( std::ostream &amp;os,  mlnorm&lt;sq_T&gt; &amp;ml ) {
604<a name="l00826"></a>00826         os &lt;&lt; <span class="stringliteral">"A:"</span>&lt;&lt; ml.A&lt;&lt;endl;
605<a name="l00827"></a>00827         os &lt;&lt; <span class="stringliteral">"mu:"</span>&lt;&lt; ml.mu_const&lt;&lt;endl;
606<a name="l00828"></a>00828         os &lt;&lt; <span class="stringliteral">"R:"</span> &lt;&lt; ml.epdf._R().to_mat() &lt;&lt;endl;
607<a name="l00829"></a>00829         <span class="keywordflow">return</span> os;
608<a name="l00830"></a>00830 };
609<a name="l00831"></a>00831
610<a name="l00832"></a>00832 }
611<a name="l00833"></a>00833 <span class="preprocessor">#endif //EF_H</span>
[91]612</pre></div></div>
[255]613<hr size="1"><address style="text-align: right;"><small>Generated on Tue Jan 27 16:29:53 2009 for mixpp by&nbsp;
[8]614<a href="http://www.doxygen.org/index.html">
[139]615<img src="doxygen.png" alt="doxygen" align="middle" border="0"></a> 1.5.6 </small></address>
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