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    r234 r255  
    2626<a name="l00020"></a>00020 <span class="comment">//#include &lt;std&gt;</span> 
    2727<a name="l00021"></a>00021  
    28 <a name="l00022"></a>00022 <span class="keyword">using namespace </span>itpp; 
     28<a name="l00022"></a>00022 <span class="keyword">namespace </span>bdm{ 
    2929<a name="l00023"></a>00023  
    3030<a name="l00024"></a>00024  
    … …  
    3333<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; 
    3434<a name="l00031"></a>00031  
    35 <a name="l00038"></a><a class="code" href="classeEF.html">00038</a> <span class="keyword">class </span><a class="code" href="classeEF.html" title="General conjugate exponential family posterior density.">eEF</a> : <span class="keyword">public</span> <a class="code" href="classepdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a> { 
     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> { 
    3636<a name="l00039"></a>00039 <span class="keyword">public</span>: 
    3737<a name="l00040"></a>00040 <span class="comment">//      eEF() :epdf() {};</span> 
    38 <a name="l00042"></a><a class="code" href="classeEF.html#7e3c63655e8375c76bf1f421245427a7">00042</a> <span class="comment"></span>        <a class="code" href="classeEF.html#7e3c63655e8375c76bf1f421245427a7" title="default constructor">eEF</a> ( <span class="keyword">const</span> <a class="code" href="classRV.html" title="Class representing variables, most often random variables.">RV</a> &amp;<a class="code" href="classepdf.html#74da992e3f5d598da8850b646b79b9d9" title="Identified of the random variable.">rv</a> ) :<a class="code" href="classepdf.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="classeEF.html#69e5680dac10375d62520d26c672477d" title="logarithm of the normalizing constant, ">lognc</a>() <span class="keyword">const</span> =0; 
    40 <a name="l00046"></a><a class="code" href="classeEF.html#a89bef8996410609004fa019b5b48964">00046</a>         <span class="keyword">virtual</span> <span class="keywordtype">void</span> <a class="code" href="classeEF.html#a89bef8996410609004fa019b5b48964" 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="classeEF.html#41c70565b4d3fb424599817d008f0c71">00048</a>         <span class="keyword">virtual</span> <span class="keywordtype">double</span> <a class="code" href="classeEF.html#41c70565b4d3fb424599817d008f0c71" 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="classeEF.html#357512dd565e199904d367294b7dd862">00050</a>         <span class="keyword">virtual</span> <span class="keywordtype">double</span> <a class="code" href="classeEF.html#357512dd565e199904d367294b7dd862" 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="classeEF.html#41c70565b4d3fb424599817d008f0c71" title="Evaluate normalized log-probability.">evallog_nn</a> ( val )-<a class="code" href="classeEF.html#69e5680dac10375d62520d26c672477d" 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="classeEF.html#cff03a658aec11b806c3e3d48f37b81f">00052</a>         <span class="keyword">virtual</span> vec <a class="code" href="classeEF.html#357512dd565e199904d367294b7dd862" title="Evaluate normalized log-probability.">evallog</a> ( <span class="keyword">const</span> mat &amp;Val )<span class="keyword"> const </span>{ 
     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>{ 
    4444<a name="l00053"></a>00053                 vec x ( Val.cols() ); 
    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="classeEF.html#41c70565b4d3fb424599817d008f0c71" 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="classeEF.html#69e5680dac10375d62520d26c672477d" title="logarithm of the normalizing constant, ">lognc</a>(); 
     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>(); 
    4747<a name="l00056"></a>00056         } 
    48 <a name="l00058"></a><a class="code" href="classeEF.html#4f8385dd1cc9740522dc373b1dc3cbf5">00058</a>         <span class="keyword">virtual</span> <span class="keywordtype">void</span> <a class="code" href="classeEF.html#4f8385dd1cc9740522dc373b1dc3cbf5" 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> );}; 
     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> );}; 
    4949<a name="l00059"></a>00059 }; 
    5050<a name="l00060"></a>00060  
    51 <a name="l00067"></a><a class="code" href="classmEF.html">00067</a> <span class="keyword">class </span><a class="code" href="classmEF.html" title="Exponential family model.">mEF</a> : <span class="keyword">public</span> <a class="code" href="classmpdf.html" title="Conditional probability density, e.g. modeling some dependencies.">mpdf</a> { 
     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> { 
    5252<a name="l00068"></a>00068  
    5353<a name="l00069"></a>00069 <span class="keyword">public</span>: 
    54 <a name="l00071"></a><a class="code" href="classmEF.html#8bf51fe8654d7b83c8c8afeb19409d4f">00071</a>         <a class="code" href="classmEF.html#8bf51fe8654d7b83c8c8afeb19409d4f" title="Default constructor.">mEF</a> ( <span class="keyword">const</span> <a class="code" href="classRV.html" title="Class representing variables, most often random variables.">RV</a> &amp;rv0, <span class="keyword">const</span> <a class="code" href="classRV.html" title="Class representing variables, most often random variables.">RV</a> &amp;rvc0 ) :<a class="code" href="classmpdf.html" title="Conditional probability density, e.g. modeling some dependencies.">mpdf</a> ( rv0,rvc0 ) {}; 
     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 ) {}; 
    5555<a name="l00072"></a>00072 }; 
    5656<a name="l00073"></a>00073  
    57 <a name="l00075"></a><a class="code" href="classBMEF.html">00075</a> <span class="keyword">class </span><a class="code" href="classBMEF.html" title="Estimator for Exponential family.">BMEF</a> : <span class="keyword">public</span> <a class="code" href="classBM.html" title="Bayesian Model of the world, i.e. all uncertainty is modeled by probabilities.">BM</a> { 
     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> { 
    5858<a name="l00076"></a>00076 <span class="keyword">protected</span>: 
    59 <a name="l00078"></a><a class="code" href="classBMEF.html#538d632e59f9afa8daa1de74da12ce71">00078</a>         <span class="keywordtype">double</span> <a class="code" href="classBMEF.html#538d632e59f9afa8daa1de74da12ce71" title="forgetting factor">frg</a>; 
    60 <a name="l00080"></a><a class="code" href="classBMEF.html#308cf5d4133cd471fdf1ecd5dfa09d02">00080</a>         <span class="keywordtype">double</span> <a class="code" href="classBMEF.html#308cf5d4133cd471fdf1ecd5dfa09d02" title="cached value of lognc() in the previous step (used in evaluation of ll )">last_lognc</a>; 
     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>; 
    6161<a name="l00081"></a>00081 <span class="keyword">public</span>: 
    62 <a name="l00083"></a><a class="code" href="classBMEF.html#46ac5c919ae647f3a6a38d9faba35f5d">00083</a>         <a class="code" href="classBMEF.html#46ac5c919ae647f3a6a38d9faba35f5d" title="Default constructor.">BMEF</a> ( <span class="keyword">const</span> <a class="code" href="classRV.html" title="Class representing variables, most often random variables.">RV</a> &amp;<a class="code" href="classBM.html#af00f0612fabe66241dd507188cdbf88" title="Random variable of the posterior.">rv</a>, <span class="keywordtype">double</span> frg0=1.0 ) :<a class="code" href="classBM.html" title="Bayesian Model of the world, i.e. all uncertainty is modeled by probabilities.">BM</a> ( rv ), <a class="code" href="classBMEF.html#538d632e59f9afa8daa1de74da12ce71" title="forgetting factor">frg</a> ( frg0 ) {} 
    63 <a name="l00085"></a><a class="code" href="classBMEF.html#3dc6277cafbdc6cbc2db860ff219b33e">00085</a>         <a class="code" href="classBMEF.html#46ac5c919ae647f3a6a38d9faba35f5d" title="Default constructor.">BMEF</a> ( <span class="keyword">const</span> <a class="code" href="classBMEF.html" title="Estimator for Exponential family.">BMEF</a> &amp;B ) :<a class="code" href="classBM.html" title="Bayesian Model of the world, i.e. all uncertainty is modeled by probabilities.">BM</a> ( B ), <a class="code" href="classBMEF.html#538d632e59f9afa8daa1de74da12ce71" title="forgetting factor">frg</a> ( B.<a class="code" href="classBMEF.html#538d632e59f9afa8daa1de74da12ce71" title="forgetting factor">frg</a> ), <a class="code" href="classBMEF.html#308cf5d4133cd471fdf1ecd5dfa09d02" title="cached value of lognc() in the previous step (used in evaluation of ll )">last_lognc</a> ( B.<a class="code" href="classBMEF.html#308cf5d4133cd471fdf1ecd5dfa09d02" 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="classBMEF.html#30bb40eb1fd31869b2e62e79e1ecdcb4">00087</a>         <span class="keyword">virtual</span> <span class="keywordtype">void</span> <a class="code" href="classBMEF.html#30bb40eb1fd31869b2e62e79e1ecdcb4" title="get statistics from another model">set_statistics</a> ( <span class="keyword">const</span> <a class="code" href="classBMEF.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="classBMEF.html#8f4ecb6e2eaf630155a1fa98f35aa6ad">00089</a>         <span class="keyword">virtual</span> <span class="keywordtype">void</span> <a class="code" href="classBMEF.html#8f4ecb6e2eaf630155a1fa98f35aa6ad" 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 ) {}; 
     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 ) {}; 
    6666<a name="l00090"></a>00090         <span class="comment">//original Bayes</span> 
    67 <a name="l00091"></a>00091         <span class="keywordtype">void</span> <a class="code" href="classBMEF.html#8f4ecb6e2eaf630155a1fa98f35aa6ad" 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="classBMEF.html#b3689f3ade328d157aa813384a5b153a">00093</a>         <span class="keyword">virtual</span> <span class="keywordtype">void</span> <a class="code" href="classBMEF.html#b3689f3ade328d157aa813384a5b153a" title="Flatten the posterior according to the given BMEF (of the same type!).">flatten</a> ( <span class="keyword">const</span> <a class="code" href="classBMEF.html" title="Estimator for Exponential family.">BMEF</a> * B ) {it_error ( <span class="stringliteral">"Not implemented"</span> );} 
     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> );} 
    6969<a name="l00095"></a>00095 <span class="comment">//      virtual void flatten ( double nu0 ) {it_error ( "Not implemented" );}</span> 
    7070<a name="l00096"></a>00096  
    71 <a name="l00097"></a><a class="code" href="classBMEF.html#97f5312efe4a5bedb86d2daec59d8651">00097</a>         <a class="code" href="classBMEF.html" title="Estimator for Exponential family.">BMEF</a>* <a class="code" href="classBMEF.html#97f5312efe4a5bedb86d2daec59d8651" 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;}; 
     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;}; 
    7272<a name="l00098"></a>00098 }; 
    7373<a name="l00099"></a>00099  
    7474<a name="l00100"></a>00100 <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt; 
    75 <a name="l00101"></a>00101 <span class="keyword">class </span><a class="code" href="classmlnorm.html" title="Normal distributed linear function with linear function of mean value;.">mlnorm</a>; 
     75<a name="l00101"></a>00101 <span class="keyword">class </span>mlnorm; 
    7676<a name="l00102"></a>00102  
    7777<a name="l00108"></a>00108 <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt; 
    78 <a name="l00109"></a><a class="code" href="classenorm.html">00109</a> <span class="keyword">class </span><a class="code" href="classenorm.html" title="Gaussian density with positive definite (decomposed) covariance matrix.">enorm</a> : <span class="keyword">public</span> <a class="code" href="classeEF.html" title="General conjugate exponential family posterior density.">eEF</a> { 
     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> { 
    7979<a name="l00110"></a>00110 <span class="keyword">protected</span>: 
    80 <a name="l00112"></a><a class="code" href="classenorm.html#71fde0d54bba147e00f612577f95ad20">00112</a>         vec <a class="code" href="classenorm.html#71fde0d54bba147e00f612577f95ad20" title="mean value">mu</a>; 
    81 <a name="l00114"></a><a class="code" href="classenorm.html#4ccc8d8514d644ef1c98d8ab023748a1">00114</a>         sq_T <a class="code" href="classenorm.html#4ccc8d8514d644ef1c98d8ab023748a1" title="Covariance matrix in decomposed form.">R</a>; 
    82 <a name="l00116"></a><a class="code" href="classenorm.html#6938fc390a19cdaf6ad4503fcbaada4e">00116</a>         <span class="keywordtype">int</span> <a class="code" href="classenorm.html#6938fc390a19cdaf6ad4503fcbaada4e" title="dimension (redundant from rv.count() for easier coding )">dim</a>; 
     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>; 
    8383<a name="l00117"></a>00117 <span class="keyword">public</span>: 
    84 <a name="l00119"></a>00119         <a class="code" href="classenorm.html#0caf54fed9e48f9fe28b534b2027df2f" title="Default constructor.">enorm</a> ( <span class="keyword">const</span> <a class="code" href="classRV.html" title="Class representing variables, most often random variables.">RV</a> &amp;<a class="code" href="classepdf.html#74da992e3f5d598da8850b646b79b9d9" title="Identified of the random variable.">rv</a> ); 
    85 <a name="l00121"></a>00121         <span class="keywordtype">void</span> <a class="code" href="classenorm.html#1394a65caa6e00d42e00cc99b12227af" title="Set mean value mu and covariance R.">set_parameters</a> ( <span class="keyword">const</span> vec &amp;<a class="code" href="classenorm.html#71fde0d54bba147e00f612577f95ad20" title="mean value">mu</a>,<span class="keyword">const</span> sq_T &amp;<a class="code" href="classenorm.html#4ccc8d8514d644ef1c98d8ab023748a1" title="Covariance matrix in decomposed form.">R</a> ); 
     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> ); 
    8686<a name="l00123"></a>00123         <span class="comment">//void tupdate ( double phi, mat &amp;vbar, double nubar );</span> 
    87 <a name="l00125"></a>00125 <span class="comment"></span>        <span class="keywordtype">void</span> <a class="code" href="classenorm.html#5bf185e31e5954fceb90ada3debd2ff2" title="dupdate in exponential form (not really handy)">dupdate</a> ( mat &amp;v,<span class="keywordtype">double</span> nu=1.0 ); 
     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 ); 
    8888<a name="l00126"></a>00126  
    89 <a name="l00127"></a>00127         vec <a class="code" href="classenorm.html#60b47544f6181ffd4530d3e415ce12c5" title="Returns a sample,  from density .">sample</a>() <span class="keyword">const</span>; 
    90 <a name="l00129"></a>00129         mat <a class="code" href="classenorm.html#60b47544f6181ffd4530d3e415ce12c5" title="Returns a sample,  from density .">sample</a> ( <span class="keywordtype">int</span> N ) <span class="keyword">const</span>; 
     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>; 
    9191<a name="l00130"></a>00130 <span class="comment">//      double eval ( const vec &amp;val ) const ;</span> 
    92 <a name="l00131"></a>00131         <span class="keywordtype">double</span> <a class="code" href="classenorm.html#50cb0a083d97a7adbbd97c92e712c46c" 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="classenorm.html#b289a36a69db59d182bb6eba9c05d4a8" title="logarithm of the normalizing constant, ">lognc</a> () <span class="keyword">const</span>; 
    94 <a name="l00133"></a><a class="code" href="classenorm.html#50fa84da7bae02f7af17a98f37566899">00133</a>         vec <a class="code" href="classenorm.html#50fa84da7bae02f7af17a98f37566899" title="return expected value">mean</a>()<span class="keyword"> const </span>{<span class="keywordflow">return</span> <a class="code" href="classenorm.html#71fde0d54bba147e00f612577f95ad20" title="mean value">mu</a>;} 
    95 <a name="l00134"></a><a class="code" href="classenorm.html#d4b4bfec464fe971bf57e69d71a1cf2e">00134</a>         vec <a class="code" href="classenorm.html#d4b4bfec464fe971bf57e69d71a1cf2e" title="return expected variance (not covariance!)">variance</a>()<span class="keyword"> const </span>{<span class="keywordflow">return</span> diag(<a class="code" href="classenorm.html#4ccc8d8514d644ef1c98d8ab023748a1" title="Covariance matrix in decomposed form.">R</a>.to_mat());} 
     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());} 
    9696<a name="l00135"></a>00135 <span class="comment">//      mlnorm&lt;sq_T&gt;* condition ( const RV &amp;rvn ) const ;</span> 
    97 <a name="l00136"></a>00136         <a class="code" href="classmpdf.html" title="Conditional probability density, e.g. modeling some dependencies.">mpdf</a>* <a class="code" href="classenorm.html#921024bd6d5a0e65f2af2e39bf38dfca" 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="classRV.html" title="Class representing variables, most often random variables.">RV</a> &amp;rvn ) <span class="keyword">const</span> ; 
     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> ; 
    9898<a name="l00137"></a>00137 <span class="comment">//      enorm&lt;sq_T&gt;* marginal ( const RV &amp;rv ) const;</span> 
    99 <a name="l00138"></a>00138         <a class="code" href="classepdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a>* <a class="code" href="classenorm.html#af50a6102846060bcb23a670bf38117b" 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="classRV.html" title="Class representing variables, most often random variables.">RV</a> &amp;<a class="code" href="classepdf.html#74da992e3f5d598da8850b646b79b9d9" title="Identified of the random variable.">rv</a> ) <span class="keyword">const</span>; 
     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>; 
    100100<a name="l00139"></a>00139 <span class="comment">//Access methods</span> 
    101 <a name="l00141"></a><a class="code" href="classenorm.html#0b8cb284e5af920a1b64a21d057ec5ac">00141</a> <span class="comment"></span>        vec&amp; <a class="code" href="classenorm.html#0b8cb284e5af920a1b64a21d057ec5ac" title="returns a pointer to the internal mean value. Use with Care!">_mu</a>() {<span class="keywordflow">return</span> <a class="code" href="classenorm.html#71fde0d54bba147e00f612577f95ad20" title="mean value">mu</a>;} 
     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>;} 
    102102<a name="l00142"></a>00142  
    103 <a name="l00144"></a><a class="code" href="classenorm.html#d892a38f03be12e572ea57d9689cef6b">00144</a>         <span class="keywordtype">void</span> <a class="code" href="classenorm.html#d892a38f03be12e572ea57d9689cef6b" title="access function">set_mu</a> ( <span class="keyword">const</span> vec mu0 ) { <a class="code" href="classenorm.html#71fde0d54bba147e00f612577f95ad20" title="mean value">mu</a>=mu0;} 
     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;} 
    104104<a name="l00145"></a>00145  
    105 <a name="l00147"></a><a class="code" href="classenorm.html#7a5034b25771a84450a990d10fc40ac9">00147</a>         sq_T&amp; <a class="code" href="classenorm.html#7a5034b25771a84450a990d10fc40ac9" title="returns pointers to the internal variance and its inverse. Use with Care!">_R</a>() {<span class="keywordflow">return</span> <a class="code" href="classenorm.html#4ccc8d8514d644ef1c98d8ab023748a1" 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="classenorm.html#7a5034b25771a84450a990d10fc40ac9" 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="classenorm.html#4ccc8d8514d644ef1c98d8ab023748a1" title="Covariance matrix in decomposed form.">R</a>;} 
     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>;} 
    107107<a name="l00149"></a>00149  
    108108<a name="l00151"></a>00151 <span class="comment">//      mat getR () {return R.to_mat();}</span> 
    109109<a name="l00152"></a>00152 }; 
    110110<a name="l00153"></a>00153  
    111 <a name="l00160"></a><a class="code" href="classegiw.html">00160</a> <span class="keyword">class </span><a class="code" href="classegiw.html" title="Gauss-inverse-Wishart density stored in LD form.">egiw</a> : <span class="keyword">public</span> <a class="code" href="classeEF.html" title="General conjugate exponential family posterior density.">eEF</a> { 
     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> { 
    112112<a name="l00161"></a>00161 <span class="keyword">protected</span>: 
    113 <a name="l00163"></a><a class="code" href="classegiw.html#f343d03ede89db820edf44a6297fa442">00163</a>         <a class="code" href="classldmat.html" title="Matrix stored in LD form, (commonly known as UD).">ldmat</a> <a class="code" href="classegiw.html#f343d03ede89db820edf44a6297fa442" title="Extended information matrix of sufficient statistics.">V</a>; 
    114 <a name="l00165"></a><a class="code" href="classegiw.html#4a2f130b91afe84f6d62fed289d5d453">00165</a>         <span class="keywordtype">double</span> <a class="code" href="classegiw.html#4a2f130b91afe84f6d62fed289d5d453" title="Number of data records (degrees of freedom) of sufficient statistics.">nu</a>; 
    115 <a name="l00167"></a><a class="code" href="classegiw.html#3d5c719f15a5527a6c62c2a53160148e">00167</a>         <span class="keywordtype">int</span> <a class="code" href="classegiw.html#3d5c719f15a5527a6c62c2a53160148e" title="Dimension of the output.">xdim</a>; 
    116 <a name="l00169"></a><a class="code" href="classegiw.html#c70d13d86e0d9f0acede3e1dc0368812">00169</a>         <span class="keywordtype">int</span> <a class="code" href="classegiw.html#c70d13d86e0d9f0acede3e1dc0368812" title="Dimension of the regressor.">nPsi</a>; 
     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>; 
    117117<a name="l00170"></a>00170 <span class="keyword">public</span>: 
    118 <a name="l00172"></a><a class="code" href="classegiw.html#056c094f01ca1cc308d72162f47617c9">00172</a>         <a class="code" href="classegiw.html#056c094f01ca1cc308d72162f47617c9" title="Default constructor, if nu0&amp;lt;0 a minimal nu0 will be computed.">egiw</a> ( <a class="code" href="classRV.html" title="Class representing variables, most often random variables.">RV</a> <a class="code" href="classepdf.html#74da992e3f5d598da8850b646b79b9d9" title="Identified of the random variable.">rv</a>, mat V0, <span class="keywordtype">double</span> nu0=-1.0 ) : <a class="code" href="classeEF.html" title="General conjugate exponential family posterior density.">eEF</a> ( rv ), <a class="code" href="classegiw.html#f343d03ede89db820edf44a6297fa442" title="Extended information matrix of sufficient statistics.">V</a> ( V0 ), <a class="code" href="classegiw.html#4a2f130b91afe84f6d62fed289d5d453" title="Number of data records (degrees of freedom) of sufficient statistics.">nu</a> ( nu0 ) { 
    119 <a name="l00173"></a>00173                 <a class="code" href="classegiw.html#3d5c719f15a5527a6c62c2a53160148e" title="Dimension of the output.">xdim</a> = rv.<a class="code" href="classRV.html#f5c7b8bd589eef09ccdf3329a0addea0" title="Return number of scalars in the RV.">count</a>() /<a class="code" href="classegiw.html#f343d03ede89db820edf44a6297fa442" 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="classRV.html#f5c7b8bd589eef09ccdf3329a0addea0" title="Return number of scalars in the RV.">count</a>() ==<a class="code" href="classegiw.html#3d5c719f15a5527a6c62c2a53160148e" title="Dimension of the output.">xdim</a>*<a class="code" href="classegiw.html#f343d03ede89db820edf44a6297fa442" 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="classegiw.html#c70d13d86e0d9f0acede3e1dc0368812" title="Dimension of the regressor.">nPsi</a> = <a class="code" href="classegiw.html#f343d03ede89db820edf44a6297fa442" 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="classegiw.html#3d5c719f15a5527a6c62c2a53160148e" title="Dimension of the output.">xdim</a>; 
     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>; 
    122122<a name="l00176"></a>00176                 <span class="comment">//set mu to have proper normalization and </span> 
    123123<a name="l00177"></a>00177                 <span class="keywordflow">if</span> (nu0&lt;0){ 
    124 <a name="l00178"></a>00178                         <a class="code" href="classegiw.html#4a2f130b91afe84f6d62fed289d5d453" title="Number of data records (degrees of freedom) of sufficient statistics.">nu</a> = 0.1 +nPsi +2*<a class="code" href="classegiw.html#3d5c719f15a5527a6c62c2a53160148e" title="Dimension of the output.">xdim</a> +2; <span class="comment">// +2 assures finite expected value of R</span> 
     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> 
    125125<a name="l00179"></a>00179                         <span class="comment">// terms before that are sufficient for finite normalization</span> 
    126126<a name="l00180"></a>00180                 } 
    127127<a name="l00181"></a>00181         } 
    128 <a name="l00183"></a><a class="code" href="classegiw.html#18c1bf6125652a6dcbca68dd02dddd8d">00183</a>         <a class="code" href="classegiw.html#056c094f01ca1cc308d72162f47617c9" title="Default constructor, if nu0&amp;lt;0 a minimal nu0 will be computed.">egiw</a> ( <a class="code" href="classRV.html" title="Class representing variables, most often random variables.">RV</a> <a class="code" href="classepdf.html#74da992e3f5d598da8850b646b79b9d9" 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="classeEF.html" title="General conjugate exponential family posterior density.">eEF</a> ( rv ), <a class="code" href="classegiw.html#f343d03ede89db820edf44a6297fa442" title="Extended information matrix of sufficient statistics.">V</a> ( V0 ), <a class="code" href="classegiw.html#4a2f130b91afe84f6d62fed289d5d453" title="Number of data records (degrees of freedom) of sufficient statistics.">nu</a> ( nu0 ) { 
    129 <a name="l00184"></a>00184                 <a class="code" href="classegiw.html#3d5c719f15a5527a6c62c2a53160148e" title="Dimension of the output.">xdim</a> = rv.<a class="code" href="classRV.html#f5c7b8bd589eef09ccdf3329a0addea0" title="Return number of scalars in the RV.">count</a>() /<a class="code" href="classegiw.html#f343d03ede89db820edf44a6297fa442" 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="classRV.html#f5c7b8bd589eef09ccdf3329a0addea0" title="Return number of scalars in the RV.">count</a>() ==<a class="code" href="classegiw.html#3d5c719f15a5527a6c62c2a53160148e" title="Dimension of the output.">xdim</a>*<a class="code" href="classegiw.html#f343d03ede89db820edf44a6297fa442" 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="classegiw.html#c70d13d86e0d9f0acede3e1dc0368812" title="Dimension of the regressor.">nPsi</a> = <a class="code" href="classegiw.html#f343d03ede89db820edf44a6297fa442" 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="classegiw.html#3d5c719f15a5527a6c62c2a53160148e" title="Dimension of the output.">xdim</a>; 
     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>; 
    132132<a name="l00187"></a>00187                 <span class="keywordflow">if</span> (nu0&lt;0){ 
    133 <a name="l00188"></a>00188                         <a class="code" href="classegiw.html#4a2f130b91afe84f6d62fed289d5d453" title="Number of data records (degrees of freedom) of sufficient statistics.">nu</a> = 0.1 +nPsi +2*<a class="code" href="classegiw.html#3d5c719f15a5527a6c62c2a53160148e" title="Dimension of the output.">xdim</a> +2; <span class="comment">// +2 assures finite expected value of R</span> 
     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> 
    134134<a name="l00189"></a>00189                         <span class="comment">// terms before that are sufficient for finite normalization</span> 
    135135<a name="l00190"></a>00190                 } 
    136136<a name="l00191"></a>00191         } 
    137137<a name="l00192"></a>00192  
    138 <a name="l00193"></a>00193         vec <a class="code" href="classegiw.html#3d2c1f2ba0f9966781f1e0ae695e8a6f" title="Returns a sample,  from density .">sample</a>() <span class="keyword">const</span>; 
    139 <a name="l00194"></a>00194         vec <a class="code" href="classegiw.html#6deb0ff2859f41ef7cbdf6a842cabb29" title="return expected value">mean</a>() <span class="keyword">const</span>; 
    140 <a name="l00195"></a><a class="code" href="classegiw.html#458a89e32dfcc363daa4b6d5335ac791">00195</a>         vec <a class="code" href="classegiw.html#458a89e32dfcc363daa4b6d5335ac791" 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);}; 
     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);}; 
    141141<a name="l00196"></a>00196         <span class="keywordtype">void</span> mean_mat ( mat &amp;M, mat&amp;R ) <span class="keyword">const</span>; 
    142 <a name="l00198"></a>00198         <span class="keywordtype">double</span> <a class="code" href="classegiw.html#2d94daac10d66bb743e4ddc8c1ba7268" 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="classegiw.html#70eb1a0b88459b227f919b425b0d3359" title="logarithm of the normalizing constant, ">lognc</a> () <span class="keyword">const</span>; 
     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>; 
    144144<a name="l00200"></a>00200  
    145145<a name="l00201"></a>00201         <span class="comment">//Access</span> 
    146 <a name="l00203"></a><a class="code" href="classegiw.html#533e792e1175bfa06d5d595dc5d080d5">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="classegiw.html#533e792e1175bfa06d5d595dc5d080d5" title="returns a pointer to the internal statistics. Use with Care!">_V</a>() {<span class="keywordflow">return</span> <a class="code" href="classegiw.html#f343d03ede89db820edf44a6297fa442" title="Extended information matrix of sufficient statistics.">V</a>;} 
    147 <a name="l00205"></a><a class="code" href="classegiw.html#a46c8a206edf80b357a138d7491780c1">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="classegiw.html#533e792e1175bfa06d5d595dc5d080d5" 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="classegiw.html#f343d03ede89db820edf44a6297fa442" title="Extended information matrix of sufficient statistics.">V</a>;} 
    148 <a name="l00207"></a><a class="code" href="classegiw.html#08029c481ff95d24f093df0573879afe">00207</a>         <span class="keywordtype">double</span>&amp; <a class="code" href="classegiw.html#08029c481ff95d24f093df0573879afe" title="returns a pointer to the internal statistics. Use with Care!">_nu</a>()  {<span class="keywordflow">return</span> <a class="code" href="classegiw.html#4a2f130b91afe84f6d62fed289d5d453" 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="classegiw.html#08029c481ff95d24f093df0573879afe" 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="classegiw.html#4a2f130b91afe84f6d62fed289d5d453" title="Number of data records (degrees of freedom) of sufficient statistics.">nu</a>;} 
    150 <a name="l00209"></a><a class="code" href="classegiw.html#036306322a90a9977834baac07460816">00209</a>         <span class="keywordtype">void</span> <a class="code" href="classegiw.html#036306322a90a9977834baac07460816" title="Power of the density, used e.g. to flatten the density.">pow</a> ( <span class="keywordtype">double</span> p ) {<a class="code" href="classegiw.html#f343d03ede89db820edf44a6297fa442" title="Extended information matrix of sufficient statistics.">V</a>*=p;<a class="code" href="classegiw.html#4a2f130b91afe84f6d62fed289d5d453" title="Number of data records (degrees of freedom) of sufficient statistics.">nu</a>*=p;}; 
     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;}; 
    151151<a name="l00210"></a>00210 }; 
    152152<a name="l00211"></a>00211  
    153 <a name="l00220"></a><a class="code" href="classeDirich.html">00220</a> <span class="keyword">class </span><a class="code" href="classeDirich.html" title="Dirichlet posterior density.">eDirich</a>: <span class="keyword">public</span> <a class="code" href="classeEF.html" title="General conjugate exponential family posterior density.">eEF</a> { 
     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> { 
    154154<a name="l00221"></a>00221 <span class="keyword">protected</span>: 
    155 <a name="l00223"></a><a class="code" href="classeDirich.html#15e6b65e9595eedc8a1286c6cecd36d7">00223</a>         vec <a class="code" href="classeDirich.html#15e6b65e9595eedc8a1286c6cecd36d7" title="sufficient statistics">beta</a>; 
    156 <a name="l00225"></a><a class="code" href="classeDirich.html#a4f34a1b98ee6d09688b8d0f043ac10d">00225</a>         <span class="keywordtype">double</span> <a class="code" href="classeDirich.html#a4f34a1b98ee6d09688b8d0f043ac10d" title="speedup variable">gamma</a>; 
     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>; 
    157157<a name="l00226"></a>00226 <span class="keyword">public</span>: 
    158 <a name="l00228"></a><a class="code" href="classeDirich.html#ac7e6116f3575c3860d07355e96cd4af">00228</a>         <a class="code" href="classeDirich.html#ac7e6116f3575c3860d07355e96cd4af" title="Default constructor.">eDirich</a> ( <span class="keyword">const</span> <a class="code" href="classRV.html" title="Class representing variables, most often random variables.">RV</a> &amp;<a class="code" href="classepdf.html#74da992e3f5d598da8850b646b79b9d9" title="Identified of the random variable.">rv</a>, <span class="keyword">const</span> vec &amp;beta0 ) : <a class="code" href="classeEF.html" title="General conjugate exponential family posterior density.">eEF</a> ( rv ),<a class="code" href="classeDirich.html#15e6b65e9595eedc8a1286c6cecd36d7" title="sufficient statistics">beta</a> ( beta0 ) {it_assert_debug ( rv.<a class="code" href="classRV.html#f5c7b8bd589eef09ccdf3329a0addea0" title="Return number of scalars in the RV.">count</a>() ==<a class="code" href="classeDirich.html#15e6b65e9595eedc8a1286c6cecd36d7" title="sufficient statistics">beta</a>.length(),<span class="stringliteral">"Incompatible statistics"</span> ); }; 
    159 <a name="l00230"></a><a class="code" href="classeDirich.html#55cccbc5eb44764dce722567acf5fd58">00230</a>         <a class="code" href="classeDirich.html#ac7e6116f3575c3860d07355e96cd4af" title="Default constructor.">eDirich</a> ( <span class="keyword">const</span> <a class="code" href="classeDirich.html" title="Dirichlet posterior density.">eDirich</a> &amp;D0 ) : <a class="code" href="classeEF.html" title="General conjugate exponential family posterior density.">eEF</a> ( D0.<a class="code" href="classepdf.html#74da992e3f5d598da8850b646b79b9d9" title="Identified of the random variable.">rv</a> ),<a class="code" href="classeDirich.html#15e6b65e9595eedc8a1286c6cecd36d7" title="sufficient statistics">beta</a> ( D0.<a class="code" href="classeDirich.html#15e6b65e9595eedc8a1286c6cecd36d7" title="sufficient statistics">beta</a> ) {}; 
    160 <a name="l00231"></a><a class="code" href="classeDirich.html#23dff79110822e9639343fe8e177fd80">00231</a>         vec <a class="code" href="classeDirich.html#23dff79110822e9639343fe8e177fd80" 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="classeDirich.html#4206e1da149d51ff3b663c9241096b73">00232</a>         vec <a class="code" href="classeDirich.html#4206e1da149d51ff3b663c9241096b73" title="return expected value">mean</a>()<span class="keyword"> const </span>{<span class="keywordflow">return</span> <a class="code" href="classeDirich.html#15e6b65e9595eedc8a1286c6cecd36d7" title="sufficient statistics">beta</a>/<a class="code" href="classeDirich.html#a4f34a1b98ee6d09688b8d0f043ac10d" title="speedup variable">gamma</a>;}; 
    162 <a name="l00233"></a><a class="code" href="classeDirich.html#eaf157ad4c0d191bc17061f29fb76da1">00233</a>         vec <a class="code" href="classeDirich.html#eaf157ad4c0d191bc17061f29fb76da1" title="return expected variance (not covariance!)">variance</a>()<span class="keyword"> const </span>{<span class="keywordflow">return</span> elem_mult(<a class="code" href="classeDirich.html#15e6b65e9595eedc8a1286c6cecd36d7" title="sufficient statistics">beta</a>,(<a class="code" href="classeDirich.html#15e6b65e9595eedc8a1286c6cecd36d7" title="sufficient statistics">beta</a>+1))/ (<a class="code" href="classeDirich.html#a4f34a1b98ee6d09688b8d0f043ac10d" title="speedup variable">gamma</a>*(<a class="code" href="classeDirich.html#a4f34a1b98ee6d09688b8d0f043ac10d" title="speedup variable">gamma</a>+1));} 
    163 <a name="l00235"></a><a class="code" href="classeDirich.html#bb4b14ed7794777386de10608a83d142">00235</a>         <span class="keywordtype">double</span> <a class="code" href="classeDirich.html#bb4b14ed7794777386de10608a83d142" 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="classeDirich.html#15e6b65e9595eedc8a1286c6cecd36d7" title="sufficient statistics">beta</a>-1 ) *log ( val );            it_assert_debug(std::isfinite(tmp),<span class="stringliteral">"Infinite value"</span>); 
     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>); 
    164164<a name="l00236"></a>00236         <span class="keywordflow">return</span> tmp;}; 
    165 <a name="l00237"></a><a class="code" href="classeDirich.html#7ce60be7119ffc639ede4e583c1f6e77">00237</a>         <span class="keywordtype">double</span> <a class="code" href="classeDirich.html#7ce60be7119ffc639ede4e583c1f6e77" title="logarithm of the normalizing constant, ">lognc</a> ()<span class="keyword"> const </span>{ 
     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>{ 
    166166<a name="l00238"></a>00238                 <span class="keywordtype">double</span> tmp; 
    167 <a name="l00239"></a>00239                 <span class="keywordtype">double</span> gam=sum ( <a class="code" href="classeDirich.html#15e6b65e9595eedc8a1286c6cecd36d7" title="sufficient statistics">beta</a> ); 
     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> ); 
    168168<a name="l00240"></a>00240                 <span class="keywordtype">double</span> lgb=0.0; 
    169 <a name="l00241"></a>00241                 <span class="keywordflow">for</span> ( <span class="keywordtype">int</span> i=0;i&lt;<a class="code" href="classeDirich.html#15e6b65e9595eedc8a1286c6cecd36d7" title="sufficient statistics">beta</a>.length();i++ ) {lgb+=lgamma ( <a class="code" href="classeDirich.html#15e6b65e9595eedc8a1286c6cecd36d7" title="sufficient statistics">beta</a> ( i ) );} 
     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 ) );} 
    170170<a name="l00242"></a>00242                 tmp= lgb-lgamma ( gam ); 
    171171<a name="l00243"></a>00243                 it_assert_debug(std::isfinite(tmp),<span class="stringliteral">"Infinite value"</span>); 
    172172<a name="l00244"></a>00244                 <span class="keywordflow">return</span> tmp; 
    173173<a name="l00245"></a>00245         }; 
    174 <a name="l00247"></a><a class="code" href="classeDirich.html#6409d0362143a23976b43641ff19e53a">00247</a>         vec&amp; <a class="code" href="classeDirich.html#6409d0362143a23976b43641ff19e53a" title="access function">_beta</a>()  {<span class="keywordflow">return</span> <a class="code" href="classeDirich.html#15e6b65e9595eedc8a1286c6cecd36d7" title="sufficient statistics">beta</a>;} 
    175 <a name="l00249"></a><a class="code" href="classeDirich.html#c842acb2e1cce5cc9000769ff06c086d">00249</a>         <span class="keywordtype">void</span> <a class="code" href="classeDirich.html#c842acb2e1cce5cc9000769ff06c086d" 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="classeDirich.html#15e6b65e9595eedc8a1286c6cecd36d7" title="sufficient statistics">beta</a>.length() ) { 
    177 <a name="l00251"></a>00251                         it_assert_debug ( <a class="code" href="classepdf.html#74da992e3f5d598da8850b646b79b9d9" title="Identified of the random variable.">rv</a>.<a class="code" href="classRV.html#c114a6f3ff06796cc2f4dacba74291eb" 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="classepdf.html#74da992e3f5d598da8850b646b79b9d9" title="Identified of the random variable.">rv</a>.<a class="code" href="classRV.html#70b24c39c5130b1e4753fa2eef495433" title="access function">set_size</a> ( 0,beta0.length() ); 
     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() ); 
    179179<a name="l00253"></a>00253                 } 
    180 <a name="l00254"></a>00254                 <a class="code" href="classeDirich.html#15e6b65e9595eedc8a1286c6cecd36d7" title="sufficient statistics">beta</a>= beta0; 
    181 <a name="l00255"></a>00255                 <a class="code" href="classeDirich.html#a4f34a1b98ee6d09688b8d0f043ac10d" title="speedup variable">gamma</a> = sum(<a class="code" href="classeDirich.html#15e6b65e9595eedc8a1286c6cecd36d7" title="sufficient statistics">beta</a>); 
     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>); 
    182182<a name="l00256"></a>00256         } 
    183183<a name="l00257"></a>00257 }; 
    184184<a name="l00258"></a>00258  
    185 <a name="l00260"></a><a class="code" href="classmultiBM.html">00260</a> <span class="keyword">class </span><a class="code" href="classmultiBM.html" title="Estimator for Multinomial density.">multiBM</a> : <span class="keyword">public</span> <a class="code" href="classBMEF.html" title="Estimator for Exponential family.">BMEF</a> { 
     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> { 
    186186<a name="l00261"></a>00261 <span class="keyword">protected</span>: 
    187 <a name="l00263"></a><a class="code" href="classmultiBM.html#eddee08a724170de63f36e40c57b27b5">00263</a>         <a class="code" href="classeDirich.html" title="Dirichlet posterior density.">eDirich</a> <a class="code" href="classmultiBM.html#eddee08a724170de63f36e40c57b27b5" title="Conjugate prior and posterior.">est</a>; 
    188 <a name="l00265"></a><a class="code" href="classmultiBM.html#7b606116aed7e8834a339cbb0424b1d6">00265</a>         vec &amp;<a class="code" href="classmultiBM.html#7b606116aed7e8834a339cbb0424b1d6" title="Pointer inside est to sufficient statistics.">beta</a>; 
     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>; 
    189189<a name="l00266"></a>00266 <span class="keyword">public</span>: 
    190 <a name="l00268"></a><a class="code" href="classmultiBM.html#7d7d7e78c129602bcde96078359dc6e5">00268</a>         <a class="code" href="classmultiBM.html#7d7d7e78c129602bcde96078359dc6e5" title="Default constructor.">multiBM</a> ( <span class="keyword">const</span> <a class="code" href="classRV.html" title="Class representing variables, most often random variables.">RV</a> &amp;<a class="code" href="classBM.html#af00f0612fabe66241dd507188cdbf88" title="Random variable of the posterior.">rv</a>, <span class="keyword">const</span> vec beta0 ) : <a class="code" href="classBMEF.html" title="Estimator for Exponential family.">BMEF</a> ( rv ),<a class="code" href="classmultiBM.html#eddee08a724170de63f36e40c57b27b5" title="Conjugate prior and posterior.">est</a> ( rv,beta0 ),<a class="code" href="classmultiBM.html#7b606116aed7e8834a339cbb0424b1d6" title="Pointer inside est to sufficient statistics.">beta</a> ( <a class="code" href="classmultiBM.html#eddee08a724170de63f36e40c57b27b5" title="Conjugate prior and posterior.">est</a>._beta() ) {<span class="keywordflow">if</span>(<a class="code" href="classmultiBM.html#7b606116aed7e8834a339cbb0424b1d6" title="Pointer inside est to sufficient statistics.">beta</a>.length()&gt;0){<a class="code" href="classBMEF.html#308cf5d4133cd471fdf1ecd5dfa09d02" title="cached value of lognc() in the previous step (used in evaluation of ll )">last_lognc</a>=<a class="code" href="classmultiBM.html#eddee08a724170de63f36e40c57b27b5" title="Conjugate prior and posterior.">est</a>.<a class="code" href="classeDirich.html#7ce60be7119ffc639ede4e583c1f6e77" title="logarithm of the normalizing constant, ">lognc</a>();}<span class="keywordflow">else</span>{<a class="code" href="classBMEF.html#308cf5d4133cd471fdf1ecd5dfa09d02" 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="classmultiBM.html#b92751adbfb9f259ca8c95232cfd9c09">00270</a>         <a class="code" href="classmultiBM.html#7d7d7e78c129602bcde96078359dc6e5" title="Default constructor.">multiBM</a> ( <span class="keyword">const</span> <a class="code" href="classmultiBM.html" title="Estimator for Multinomial density.">multiBM</a> &amp;B ) : <a class="code" href="classBMEF.html" title="Estimator for Exponential family.">BMEF</a> ( B ),<a class="code" href="classmultiBM.html#eddee08a724170de63f36e40c57b27b5" title="Conjugate prior and posterior.">est</a> ( <a class="code" href="classBM.html#af00f0612fabe66241dd507188cdbf88" title="Random variable of the posterior.">rv</a>,B.<a class="code" href="classmultiBM.html#7b606116aed7e8834a339cbb0424b1d6" title="Pointer inside est to sufficient statistics.">beta</a> ),<a class="code" href="classmultiBM.html#7b606116aed7e8834a339cbb0424b1d6" title="Pointer inside est to sufficient statistics.">beta</a> ( <a class="code" href="classmultiBM.html#eddee08a724170de63f36e40c57b27b5" title="Conjugate prior and posterior.">est</a>._beta() ) {} 
    192 <a name="l00272"></a><a class="code" href="classmultiBM.html#42e36804041e551d3ceea6c75abc0562">00272</a>         <span class="keywordtype">void</span> <a class="code" href="classmultiBM.html#42e36804041e551d3ceea6c75abc0562" title="Sets sufficient statistics to match that of givefrom mB0.">set_statistics</a> ( <span class="keyword">const</span> <a class="code" href="classBM.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="classmultiBM.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="classmultiBM.html" title="Estimator for Multinomial density.">multiBM</a>*<span class="keyword">&gt;</span> ( mB0 ); <a class="code" href="classmultiBM.html#7b606116aed7e8834a339cbb0424b1d6" title="Pointer inside est to sufficient statistics.">beta</a>=mB-&gt;<a class="code" href="classmultiBM.html#7b606116aed7e8834a339cbb0424b1d6" title="Pointer inside est to sufficient statistics.">beta</a>;} 
    193 <a name="l00273"></a><a class="code" href="classmultiBM.html#11eeba7e97954e316e959116f90d80e2">00273</a>         <span class="keywordtype">void</span> <a class="code" href="classmultiBM.html#11eeba7e97954e316e959116f90d80e2" 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="classBMEF.html#538d632e59f9afa8daa1de74da12ce71" title="forgetting factor">frg</a>&lt;1.0 ) {<a class="code" href="classmultiBM.html#7b606116aed7e8834a339cbb0424b1d6" title="Pointer inside est to sufficient statistics.">beta</a>*=<a class="code" href="classBMEF.html#538d632e59f9afa8daa1de74da12ce71" title="forgetting factor">frg</a>;<a class="code" href="classBMEF.html#308cf5d4133cd471fdf1ecd5dfa09d02" title="cached value of lognc() in the previous step (used in evaluation of ll )">last_lognc</a>=<a class="code" href="classmultiBM.html#eddee08a724170de63f36e40c57b27b5" title="Conjugate prior and posterior.">est</a>.<a class="code" href="classeDirich.html#7ce60be7119ffc639ede4e583c1f6e77" title="logarithm of the normalizing constant, ">lognc</a>();} 
    195 <a name="l00275"></a>00275                 <a class="code" href="classmultiBM.html#7b606116aed7e8834a339cbb0424b1d6" 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="classBM.html#bf6fb59b30141074f8ee1e2f43d03129" title="If true, the filter will compute likelihood of the data record and store it in ll...">evalll</a> ) {<a class="code" href="classBM.html#5623fef6572a08c2b53b8c87b82dc979" title="Logarithm of marginalized data likelihood.">ll</a>=<a class="code" href="classmultiBM.html#eddee08a724170de63f36e40c57b27b5" title="Conjugate prior and posterior.">est</a>.<a class="code" href="classeDirich.html#7ce60be7119ffc639ede4e583c1f6e77" title="logarithm of the normalizing constant, ">lognc</a>()-<a class="code" href="classBMEF.html#308cf5d4133cd471fdf1ecd5dfa09d02" title="cached value of lognc() in the previous step (used in evaluation of ll )">last_lognc</a>;} 
     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>;} 
    197197<a name="l00277"></a>00277         } 
    198 <a name="l00278"></a><a class="code" href="classmultiBM.html#13e26a61757278981fd8cac9a7ef91eb">00278</a>         <span class="keywordtype">double</span> <a class="code" href="classmultiBM.html#13e26a61757278981fd8cac9a7ef91eb">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="classeDirich.html" title="Dirichlet posterior density.">eDirich</a> pred ( <a class="code" href="classmultiBM.html#eddee08a724170de63f36e40c57b27b5" title="Conjugate prior and posterior.">est</a> ); 
    200 <a name="l00280"></a>00280                 vec &amp;<a class="code" href="classmultiBM.html#7b606116aed7e8834a339cbb0424b1d6" title="Pointer inside est to sufficient statistics.">beta</a> = pred.<a class="code" href="classeDirich.html#6409d0362143a23976b43641ff19e53a" title="access function">_beta</a>(); 
     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>(); 
    201201<a name="l00281"></a>00281  
    202202<a name="l00282"></a>00282                 <span class="keywordtype">double</span> lll; 
    203 <a name="l00283"></a>00283                 <span class="keywordflow">if</span> ( <a class="code" href="classBMEF.html#538d632e59f9afa8daa1de74da12ce71" title="forgetting factor">frg</a>&lt;1.0 ) 
    204 <a name="l00284"></a>00284                         {beta*=<a class="code" href="classBMEF.html#538d632e59f9afa8daa1de74da12ce71" title="forgetting factor">frg</a>;lll=pred.<a class="code" href="classeDirich.html#7ce60be7119ffc639ede4e583c1f6e77" title="logarithm of the normalizing constant, ">lognc</a>();} 
     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>();} 
    205205<a name="l00285"></a>00285                 <span class="keywordflow">else</span> 
    206 <a name="l00286"></a>00286                         <span class="keywordflow">if</span> ( <a class="code" href="classBM.html#bf6fb59b30141074f8ee1e2f43d03129" title="If true, the filter will compute likelihood of the data record and store it in ll...">evalll</a> ) {lll=<a class="code" href="classBMEF.html#308cf5d4133cd471fdf1ecd5dfa09d02" 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="classeDirich.html#7ce60be7119ffc639ede4e583c1f6e77" title="logarithm of the normalizing constant, ">lognc</a>();} 
     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>();} 
    208208<a name="l00288"></a>00288  
    209209<a name="l00289"></a>00289                 beta+=dt; 
    210 <a name="l00290"></a>00290                 <span class="keywordflow">return</span> pred.<a class="code" href="classeDirich.html#7ce60be7119ffc639ede4e583c1f6e77" title="logarithm of the normalizing constant, ">lognc</a>()-lll; 
     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; 
    211211<a name="l00291"></a>00291         } 
    212 <a name="l00292"></a><a class="code" href="classmultiBM.html#3988322f8f51b153622036f461f62a67">00292</a>         <span class="keywordtype">void</span> <a class="code" href="classmultiBM.html#3988322f8f51b153622036f461f62a67" title="Flatten the posterior according to the given BMEF (of the same type!).">flatten</a> ( <span class="keyword">const</span> <a class="code" href="classBMEF.html" title="Estimator for Exponential family.">BMEF</a>* B ) { 
    213 <a name="l00293"></a>00293                 <span class="keyword">const</span> <a class="code" href="classmultiBM.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="classmultiBM.html" title="Estimator for Multinomial density.">multiBM</a>*<span class="keyword">&gt;</span> ( B ); 
     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 ); 
    214214<a name="l00294"></a>00294                 <span class="comment">// sum(beta) should be equal to sum(B.beta)</span> 
    215 <a name="l00295"></a>00295                 <span class="keyword">const</span> vec &amp;Eb=E-&gt;<a class="code" href="classmultiBM.html#7b606116aed7e8834a339cbb0424b1d6" 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="classmultiBM.html#7b606116aed7e8834a339cbb0424b1d6" title="Pointer inside est to sufficient statistics.">beta</a>*= ( sum ( Eb ) /sum ( <a class="code" href="classmultiBM.html#7b606116aed7e8834a339cbb0424b1d6" title="Pointer inside est to sufficient statistics.">beta</a> ) ); 
    217 <a name="l00297"></a>00297                 <span class="keywordflow">if</span> ( <a class="code" href="classBM.html#bf6fb59b30141074f8ee1e2f43d03129" title="If true, the filter will compute likelihood of the data record and store it in ll...">evalll</a> ) {<a class="code" href="classBMEF.html#308cf5d4133cd471fdf1ecd5dfa09d02" title="cached value of lognc() in the previous step (used in evaluation of ll )">last_lognc</a>=<a class="code" href="classmultiBM.html#eddee08a724170de63f36e40c57b27b5" title="Conjugate prior and posterior.">est</a>.<a class="code" href="classeDirich.html#7ce60be7119ffc639ede4e583c1f6e77" title="logarithm of the normalizing constant, ">lognc</a>();} 
     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>();} 
    218218<a name="l00298"></a>00298         } 
    219 <a name="l00299"></a><a class="code" href="classmultiBM.html#66cdfd83a70bc281840ab0646b941684">00299</a>         <span class="keyword">const</span> <a class="code" href="classepdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a>&amp; <a class="code" href="classmultiBM.html#66cdfd83a70bc281840ab0646b941684" 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="classmultiBM.html#eddee08a724170de63f36e40c57b27b5" title="Conjugate prior and posterior.">est</a>;}; 
    220 <a name="l00300"></a><a class="code" href="classmultiBM.html#66a0fa6966e40bb6c3e7ba22d26e9d35">00300</a>         <span class="keyword">const</span> <a class="code" href="classeDirich.html" title="Dirichlet posterior density.">eDirich</a>* <a class="code" href="classmultiBM.html#66a0fa6966e40bb6c3e7ba22d26e9d35" 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="classmultiBM.html#eddee08a724170de63f36e40c57b27b5" title="Conjugate prior and posterior.">est</a>;}; 
     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>;}; 
    221221<a name="l00301"></a>00301         <span class="keywordtype">void</span> set_parameters ( <span class="keyword">const</span> vec &amp;beta0 ) { 
    222 <a name="l00302"></a>00302                 <a class="code" href="classmultiBM.html#eddee08a724170de63f36e40c57b27b5" title="Conjugate prior and posterior.">est</a>.<a class="code" href="classeDirich.html#c842acb2e1cce5cc9000769ff06c086d" title="Set internal parameters.">set_parameters</a> ( beta0 ); 
    223 <a name="l00303"></a>00303                 <a class="code" href="classBM.html#af00f0612fabe66241dd507188cdbf88" title="Random variable of the posterior.">rv</a> = <a class="code" href="classmultiBM.html#eddee08a724170de63f36e40c57b27b5" title="Conjugate prior and posterior.">est</a>.<a class="code" href="classepdf.html#ca0d32aabb4cbba347e0c37fe8607562" title="access function, possibly dangerous!">_rv</a>(); 
    224 <a name="l00304"></a>00304                 <span class="keywordflow">if</span> ( <a class="code" href="classBM.html#bf6fb59b30141074f8ee1e2f43d03129" title="If true, the filter will compute likelihood of the data record and store it in ll...">evalll</a> ) {<a class="code" href="classBMEF.html#308cf5d4133cd471fdf1ecd5dfa09d02" title="cached value of lognc() in the previous step (used in evaluation of ll )">last_lognc</a>=<a class="code" href="classmultiBM.html#eddee08a724170de63f36e40c57b27b5" title="Conjugate prior and posterior.">est</a>.<a class="code" href="classeDirich.html#7ce60be7119ffc639ede4e583c1f6e77" title="logarithm of the normalizing constant, ">lognc</a>();} 
     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>();} 
    225225<a name="l00305"></a>00305         } 
    226226<a name="l00306"></a>00306 }; 
    227227<a name="l00307"></a>00307  
    228 <a name="l00317"></a><a class="code" href="classegamma.html">00317</a> <span class="keyword">class </span><a class="code" href="classegamma.html" title="Gamma posterior density.">egamma</a> : <span class="keyword">public</span> <a class="code" href="classeEF.html" title="General conjugate exponential family posterior density.">eEF</a> { 
     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> { 
    229229<a name="l00318"></a>00318 <span class="keyword">protected</span>: 
    230 <a name="l00320"></a><a class="code" href="classegamma.html#376cebd8932546c440f21b182910b01b">00320</a>         vec <a class="code" href="classegamma.html#376cebd8932546c440f21b182910b01b" title="Vector .">alpha</a>; 
    231 <a name="l00322"></a><a class="code" href="classegamma.html#cfc5f136467488a421ab22f886323790">00322</a>         vec <a class="code" href="classegamma.html#cfc5f136467488a421ab22f886323790" title="Vector .">beta</a>; 
     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>; 
    232232<a name="l00323"></a>00323 <span class="keyword">public</span> : 
    233 <a name="l00325"></a><a class="code" href="classegamma.html#4b1d34f3b244ea51a58ec10c468788c1">00325</a>         <a class="code" href="classegamma.html#4b1d34f3b244ea51a58ec10c468788c1" title="Default constructor.">egamma</a> ( <span class="keyword">const</span> <a class="code" href="classRV.html" title="Class representing variables, most often random variables.">RV</a> &amp;<a class="code" href="classepdf.html#74da992e3f5d598da8850b646b79b9d9" title="Identified of the random variable.">rv</a> ) :<a class="code" href="classeEF.html" title="General conjugate exponential family posterior density.">eEF</a> ( rv ), <a class="code" href="classegamma.html#376cebd8932546c440f21b182910b01b" title="Vector .">alpha</a>(rv.count()), <a class="code" href="classegamma.html#cfc5f136467488a421ab22f886323790" title="Vector .">beta</a>(rv.count()) {}; 
    234 <a name="l00327"></a><a class="code" href="classegamma.html#8e348b89be82b70471fe8c5630f61339">00327</a>         <span class="keywordtype">void</span> <a class="code" href="classegamma.html#8e348b89be82b70471fe8c5630f61339" 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="classegamma.html#376cebd8932546c440f21b182910b01b" title="Vector .">alpha</a>=a,<a class="code" href="classegamma.html#cfc5f136467488a421ab22f886323790" title="Vector .">beta</a>=b;}; 
    235 <a name="l00328"></a>00328         vec <a class="code" href="classegamma.html#8e10c0021b5dfdd9cb62c6959b5ef425" title="Returns a sample,  from density .">sample</a>() <span class="keyword">const</span>; 
     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>; 
    236236<a name="l00330"></a>00330 <span class="comment">//      mat sample ( int N ) const;</span> 
    237 <a name="l00331"></a>00331         <span class="keywordtype">double</span> <a class="code" href="classegamma.html#74a49a4c696f44e54bb6b0515e155a9b" 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="classegamma.html#d6dbbdb72360f9e54d64501f80318bb6" title="logarithm of the normalizing constant, ">lognc</a> () <span class="keyword">const</span>; 
    239 <a name="l00334"></a><a class="code" href="classegamma.html#44445c56e60b91b377f207f8d5089790">00334</a>         <span class="keywordtype">void</span> <a class="code" href="classegamma.html#44445c56e60b91b377f207f8d5089790" 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="classegamma.html#376cebd8932546c440f21b182910b01b" title="Vector .">alpha</a>;b=&amp;<a class="code" href="classegamma.html#cfc5f136467488a421ab22f886323790" title="Vector .">beta</a>;}; 
    240 <a name="l00335"></a><a class="code" href="classegamma.html#6ab5ba56f7cdb2e5921c3e77524fa50a">00335</a>         vec <a class="code" href="classegamma.html#6ab5ba56f7cdb2e5921c3e77524fa50a" title="return expected value">mean</a>()<span class="keyword"> const </span>{<span class="keywordflow">return</span> elem_div(<a class="code" href="classegamma.html#376cebd8932546c440f21b182910b01b" title="Vector .">alpha</a>,<a class="code" href="classegamma.html#cfc5f136467488a421ab22f886323790" title="Vector .">beta</a>);} 
    241 <a name="l00336"></a><a class="code" href="classegamma.html#1dee6186a084565de4f9ceb3410148e4">00336</a>         vec <a class="code" href="classegamma.html#1dee6186a084565de4f9ceb3410148e4" title="return expected variance (not covariance!)">variance</a>()<span class="keyword"> const </span>{<span class="keywordflow">return</span> elem_div(<a class="code" href="classegamma.html#376cebd8932546c440f21b182910b01b" title="Vector .">alpha</a>,elem_mult(<a class="code" href="classegamma.html#cfc5f136467488a421ab22f886323790" title="Vector .">beta</a>,<a class="code" href="classegamma.html#cfc5f136467488a421ab22f886323790" title="Vector .">beta</a>)); } 
     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>)); } 
    242242<a name="l00337"></a>00337 }; 
    243243<a name="l00338"></a>00338  
    244 <a name="l00353"></a><a class="code" href="classeigamma.html">00353</a> <span class="keyword">class </span><a class="code" href="classeigamma.html" title="Inverse-Gamma posterior density.">eigamma</a> : <span class="keyword">public</span> <a class="code" href="classeEF.html" title="General conjugate exponential family posterior density.">eEF</a> { 
     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> { 
    245245<a name="l00354"></a>00354         <span class="keyword">protected</span>: 
    246 <a name="l00356"></a><a class="code" href="classeigamma.html#ea00e33f405ebd918e06cede968a735b">00356</a>                 vec* <a class="code" href="classeigamma.html#ea00e33f405ebd918e06cede968a735b" title="Vector .">alpha</a>; 
    247 <a name="l00358"></a><a class="code" href="classeigamma.html#ee446ec667a4df391e0db41decb2d558">00358</a>                 vec* <a class="code" href="classeigamma.html#ee446ec667a4df391e0db41decb2d558" 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="classeigamma.html#906f2a3a8fbf08b2af49776f2f1be5d4">00360</a>                 <a class="code" href="classegamma.html" title="Gamma posterior density.">egamma</a> <a class="code" href="classeigamma.html#906f2a3a8fbf08b2af49776f2f1be5d4" title="internal egamma">eg</a>; 
     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>; 
    249249<a name="l00361"></a>00361         <span class="keyword">public</span> : 
    250 <a name="l00363"></a><a class="code" href="classeigamma.html#ea0edc0a1f32350219f55cf35d83a5f6">00363</a>                 <a class="code" href="classeigamma.html#ea0edc0a1f32350219f55cf35d83a5f6" title="Default constructor.">eigamma</a> ( <span class="keyword">const</span> <a class="code" href="classRV.html" title="Class representing variables, most often random variables.">RV</a> &amp;<a class="code" href="classepdf.html#74da992e3f5d598da8850b646b79b9d9" title="Identified of the random variable.">rv</a> ) :<a class="code" href="classeEF.html" title="General conjugate exponential family posterior density.">eEF</a> ( rv ), <a class="code" href="classeigamma.html#906f2a3a8fbf08b2af49776f2f1be5d4" title="internal egamma">eg</a>(rv) {<a class="code" href="classeigamma.html#906f2a3a8fbf08b2af49776f2f1be5d4" title="internal egamma">eg</a>.<a class="code" href="classegamma.html#44445c56e60b91b377f207f8d5089790" title="Returns poiter to alpha and beta. Potentially dengerous: use with care!">_param</a>(<a class="code" href="classeigamma.html#ea00e33f405ebd918e06cede968a735b" title="Vector .">alpha</a>,<a class="code" href="classeigamma.html#ee446ec667a4df391e0db41decb2d558" 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="classeigamma.html#a86b94a5f9189cae1b6651838dc153aa">00365</a>                 <span class="keywordtype">void</span> <a class="code" href="classeigamma.html#a86b94a5f9189cae1b6651838dc153aa" 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="classeigamma.html#ea00e33f405ebd918e06cede968a735b" title="Vector .">alpha</a>=a,*<a class="code" href="classeigamma.html#ee446ec667a4df391e0db41decb2d558" 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="classeigamma.html#b70deffdf41b590377fd6743e4d306f1">00366</a>                 vec <a class="code" href="classeigamma.html#b70deffdf41b590377fd6743e4d306f1" title="Returns a sample,  from density .">sample</a>()<span class="keyword"> const </span>{<span class="keywordflow">return</span> 1.0/<a class="code" href="classeigamma.html#906f2a3a8fbf08b2af49776f2f1be5d4" title="internal egamma">eg</a>.<a class="code" href="classegamma.html#8e10c0021b5dfdd9cb62c6959b5ef425" title="Returns a sample,  from density .">sample</a>();}; 
     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>();}; 
    253253<a name="l00368"></a>00368 <span class="comment">//      mat sample ( int N ) const;</span> 
    254 <a name="l00369"></a><a class="code" href="classeigamma.html#960cf366101389f58f11c5f748dd7e80">00369</a>                 <span class="keywordtype">double</span> <a class="code" href="classeigamma.html#960cf366101389f58f11c5f748dd7e80" 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="classeigamma.html#906f2a3a8fbf08b2af49776f2f1be5d4" title="internal egamma">eg</a>.<a class="code" href="classegamma.html#74a49a4c696f44e54bb6b0515e155a9b" title="TODO: is it used anywhere?">evallog</a>(val);}; 
    255 <a name="l00370"></a><a class="code" href="classeigamma.html#efcc280de487d8b81f9b31f286404c72">00370</a>                 <span class="keywordtype">double</span> <a class="code" href="classeigamma.html#efcc280de487d8b81f9b31f286404c72" title="logarithm of the normalizing constant, ">lognc</a> ()<span class="keyword"> const </span>{<span class="keywordflow">return</span> <a class="code" href="classeigamma.html#906f2a3a8fbf08b2af49776f2f1be5d4" title="internal egamma">eg</a>.<a class="code" href="classegamma.html#d6dbbdb72360f9e54d64501f80318bb6" title="logarithm of the normalizing constant, ">lognc</a>();}; 
    256 <a name="l00372"></a><a class="code" href="classeigamma.html#86389685695f6948d2e52070cd89a9ed">00372</a>                 <span class="keywordtype">void</span> <a class="code" href="classeigamma.html#86389685695f6948d2e52070cd89a9ed" 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="classeigamma.html#ea00e33f405ebd918e06cede968a735b" title="Vector .">alpha</a>;b=<a class="code" href="classeigamma.html#ee446ec667a4df391e0db41decb2d558" 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="classeigamma.html#0ff10e82b0f0d07c2dd4ff5f23b3c70f">00373</a>                 vec <a class="code" href="classeigamma.html#0ff10e82b0f0d07c2dd4ff5f23b3c70f" title="return expected value">mean</a>()<span class="keyword"> const </span>{<span class="keywordflow">return</span> elem_div(*<a class="code" href="classeigamma.html#ee446ec667a4df391e0db41decb2d558" title="Vector  (in fact it is 1/beta as used in definition of iG).">beta</a>,*<a class="code" href="classeigamma.html#ea00e33f405ebd918e06cede968a735b" title="Vector .">alpha</a>-1);} 
    258 <a name="l00374"></a><a class="code" href="classeigamma.html#a9ad6cb7514ffc46605f28316eda54ff">00374</a>                 vec <a class="code" href="classeigamma.html#a9ad6cb7514ffc46605f28316eda54ff" title="return expected variance (not covariance!)">variance</a>()<span class="keyword"> const </span>{vec mea=<a class="code" href="classeigamma.html#0ff10e82b0f0d07c2dd4ff5f23b3c70f" title="return expected value">mean</a>(); <span class="keywordflow">return</span> elem_div(elem_mult(mea,mea),*<a class="code" href="classeigamma.html#ea00e33f405ebd918e06cede968a735b" title="Vector .">alpha</a>-2);} 
     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);} 
    259259<a name="l00375"></a>00375 }; 
    260260<a name="l00376"></a>00376 <span class="comment">/*</span> 
    … …  
    273273<a name="l00391"></a>00391  
    274274<a name="l00393"></a>00393  
    275 <a name="l00394"></a><a class="code" href="classeuni.html">00394</a> <span class="keyword">class </span><a class="code" href="classeuni.html" title="Uniform distributed density on a rectangular support.">euni</a>: <span class="keyword">public</span> <a class="code" href="classepdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a> { 
     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> { 
    276276<a name="l00395"></a>00395 <span class="keyword">protected</span>: 
    277 <a name="l00397"></a><a class="code" href="classeuni.html#ef42cd8d7645422048d46c46ec5cdac1">00397</a>         vec <a class="code" href="classeuni.html#ef42cd8d7645422048d46c46ec5cdac1" title="lower bound on support">low</a>; 
    278 <a name="l00399"></a><a class="code" href="classeuni.html#71b6d6b41aeb61a7f76f682b72119231">00399</a>         vec <a class="code" href="classeuni.html#71b6d6b41aeb61a7f76f682b72119231" title="upper bound on support">high</a>; 
    279 <a name="l00401"></a><a class="code" href="classeuni.html#52a6ff4a54010f88a6a19fca605c64a4">00401</a>         vec <a class="code" href="classeuni.html#52a6ff4a54010f88a6a19fca605c64a4" title="internal">distance</a>; 
    280 <a name="l00403"></a><a class="code" href="classeuni.html#63105490e946e43372d6187ad1bafdda">00403</a>         <span class="keywordtype">double</span> <a class="code" href="classeuni.html#63105490e946e43372d6187ad1bafdda" title="normalizing coefficients">nk</a>; 
    281 <a name="l00405"></a><a class="code" href="classeuni.html#f445a0ce24f39d14c1a4eed53fc8e2c3">00405</a>         <span class="keywordtype">double</span> <a class="code" href="classeuni.html#f445a0ce24f39d14c1a4eed53fc8e2c3" title="cache of log( nk )">lnk</a>; 
     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>; 
    282282<a name="l00406"></a>00406 <span class="keyword">public</span>: 
    283 <a name="l00408"></a><a class="code" href="classeuni.html#2537a6c239cff52e3ba814851a1116cd">00408</a>         <a class="code" href="classeuni.html#2537a6c239cff52e3ba814851a1116cd" title="Defualt constructor.">euni</a> ( <span class="keyword">const</span> <a class="code" href="classRV.html" title="Class representing variables, most often random variables.">RV</a> <a class="code" href="classepdf.html#74da992e3f5d598da8850b646b79b9d9" title="Identified of the random variable.">rv</a> ) :<a class="code" href="classepdf.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="classeuni.html#63105490e946e43372d6187ad1bafdda" title="normalizing coefficients">nk</a>;} 
    285 <a name="l00410"></a><a class="code" href="classeuni.html#357b36417ef4c9211d12e7a4a602fd6a">00410</a>         <span class="keywordtype">double</span> <a class="code" href="classeuni.html#357b36417ef4c9211d12e7a4a602fd6a" 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="classeuni.html#f445a0ce24f39d14c1a4eed53fc8e2c3" title="cache of log( nk )">lnk</a>;} 
    286 <a name="l00411"></a><a class="code" href="classeuni.html#4a0e09392be17beaee120ba98fc038cd">00411</a>         vec <a class="code" href="classeuni.html#4a0e09392be17beaee120ba98fc038cd" 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="classepdf.html#74da992e3f5d598da8850b646b79b9d9" title="Identified of the random variable.">rv</a>.<a class="code" href="classRV.html#f5c7b8bd589eef09ccdf3329a0addea0" title="Return number of scalars in the RV.">count</a>() ); 
     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>() ); 
    288288<a name="l00413"></a>00413 <span class="preprocessor">#pragma omp critical</span> 
    289 <a name="l00414"></a>00414 <span class="preprocessor"></span>                UniRNG.sample_vector ( <a class="code" href="classepdf.html#74da992e3f5d598da8850b646b79b9d9" title="Identified of the random variable.">rv</a>.<a class="code" href="classRV.html#f5c7b8bd589eef09ccdf3329a0addea0" 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="classeuni.html#ef42cd8d7645422048d46c46ec5cdac1" title="lower bound on support">low</a>+elem_mult ( <a class="code" href="classeuni.html#52a6ff4a54010f88a6a19fca605c64a4" title="internal">distance</a>,smp ); 
     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 ); 
    291291<a name="l00416"></a>00416         } 
    292 <a name="l00418"></a><a class="code" href="classeuni.html#4fd7c6a05100616ad16ece405cad7bf2">00418</a>         <span class="keywordtype">void</span> <a class="code" href="classeuni.html#4fd7c6a05100616ad16ece405cad7bf2" 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="classeuni.html#52a6ff4a54010f88a6a19fca605c64a4" title="internal">distance</a> = high0-low0; 
    294 <a name="l00420"></a>00420                 it_assert_debug ( min ( <a class="code" href="classeuni.html#52a6ff4a54010f88a6a19fca605c64a4" title="internal">distance</a> ) &gt;0.0,<span class="stringliteral">"bad support"</span> ); 
    295 <a name="l00421"></a>00421                 <a class="code" href="classeuni.html#ef42cd8d7645422048d46c46ec5cdac1" title="lower bound on support">low</a> = low0; 
    296 <a name="l00422"></a>00422                 <a class="code" href="classeuni.html#71b6d6b41aeb61a7f76f682b72119231" title="upper bound on support">high</a> = high0; 
    297 <a name="l00423"></a>00423                 <a class="code" href="classeuni.html#63105490e946e43372d6187ad1bafdda" title="normalizing coefficients">nk</a> = prod ( 1.0/<a class="code" href="classeuni.html#52a6ff4a54010f88a6a19fca605c64a4" title="internal">distance</a> ); 
    298 <a name="l00424"></a>00424                 <a class="code" href="classeuni.html#f445a0ce24f39d14c1a4eed53fc8e2c3" title="cache of log( nk )">lnk</a> = log ( <a class="code" href="classeuni.html#63105490e946e43372d6187ad1bafdda" title="normalizing coefficients">nk</a> ); 
     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> ); 
    299299<a name="l00425"></a>00425         } 
    300 <a name="l00426"></a><a class="code" href="classeuni.html#8050087e421a9cfd1b4b1f8bd33b1cc1">00426</a>         vec <a class="code" href="classeuni.html#8050087e421a9cfd1b4b1f8bd33b1cc1" title="return expected value">mean</a>()<span class="keyword"> const </span>{<span class="keywordflow">return</span> (<a class="code" href="classeuni.html#71b6d6b41aeb61a7f76f682b72119231" title="upper bound on support">high</a>-<a class="code" href="classeuni.html#ef42cd8d7645422048d46c46ec5cdac1" title="lower bound on support">low</a>)/2.0;} 
    301 <a name="l00427"></a><a class="code" href="classeuni.html#15d7d8b2c8f13a0b1535ebc35551f01c">00427</a>         vec <a class="code" href="classeuni.html#15d7d8b2c8f13a0b1535ebc35551f01c" title="return expected variance (not covariance!)">variance</a>()<span class="keyword"> const </span>{<span class="keywordflow">return</span> (pow(<a class="code" href="classeuni.html#71b6d6b41aeb61a7f76f682b72119231" title="upper bound on support">high</a>,2)+pow(<a class="code" href="classeuni.html#ef42cd8d7645422048d46c46ec5cdac1" title="lower bound on support">low</a>,2)+elem_mult(<a class="code" href="classeuni.html#71b6d6b41aeb61a7f76f682b72119231" title="upper bound on support">high</a>,<a class="code" href="classeuni.html#ef42cd8d7645422048d46c46ec5cdac1" title="lower bound on support">low</a>))/3.0;} 
     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;} 
    302302<a name="l00428"></a>00428 }; 
    303303<a name="l00429"></a>00429  
    304304<a name="l00430"></a>00430  
    305305<a name="l00436"></a>00436 <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt; 
    306 <a name="l00437"></a><a class="code" href="classmlnorm.html">00437</a> <span class="keyword">class </span><a class="code" href="classmlnorm.html" title="Normal distributed linear function with linear function of mean value;.">mlnorm</a> : <span class="keyword">public</span> <a class="code" href="classmEF.html" title="Exponential family model.">mEF</a> { 
     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> { 
    307307<a name="l00438"></a>00438 <span class="keyword">protected</span>: 
    308 <a name="l00440"></a><a class="code" href="classmlnorm.html#b76ee2171ace4fb3ff95a131ae8fc421">00440</a>         <a class="code" href="classenorm.html" title="Gaussian density with positive definite (decomposed) covariance matrix.">enorm&lt;sq_T&gt;</a> <a class="code" href="classepdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a>; 
     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>; 
    309309<a name="l00441"></a>00441         mat A; 
    310310<a name="l00442"></a>00442         vec mu_const; 
    311311<a name="l00443"></a>00443         vec&amp; _mu; <span class="comment">//cached epdf.mu;</span> 
    312312<a name="l00444"></a>00444 <span class="keyword">public</span>: 
    313 <a name="l00446"></a>00446         <a class="code" href="classmlnorm.html#3a5ad4798d8a3878c5e93b8e796c8837" title="Constructor.">mlnorm</a> ( <span class="keyword">const</span> <a class="code" href="classRV.html" title="Class representing variables, most often random variables.">RV</a> &amp;<a class="code" href="classmpdf.html#f6687c07ff07d47812dd565368ca59eb" title="modeled random variable">rv</a>, <span class="keyword">const</span> <a class="code" href="classRV.html" title="Class representing variables, most often random variables.">RV</a> &amp;<a class="code" href="classmpdf.html#acb7dda792b3cd5576f39fa3129abbab" title="random variable in condition">rvc</a> ); 
    314 <a name="l00448"></a>00448         <span class="keywordtype">void</span> <a class="code" href="classmlnorm.html#f95dfce0b500636a44ecd7e5210de999" 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 ); 
     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 ); 
    315315<a name="l00449"></a>00449 <span class="comment">//      //!Generate one sample of the posterior</span> 
    316316<a name="l00450"></a>00450 <span class="comment">//      vec samplecond (const vec &amp;cond, double &amp;lik );</span> 
    317317<a name="l00451"></a>00451 <span class="comment">//      //!Generate matrix of samples of the posterior</span> 
    318318<a name="l00452"></a>00452 <span class="comment">//      mat samplecond (const vec &amp;cond, vec &amp;lik, int n );</span> 
    319 <a name="l00454"></a>00454 <span class="comment"></span>        <span class="keywordtype">void</span> <a class="code" href="classmlnorm.html#d41126455ac64b888a38f677886e1b40" 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 ); 
     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 ); 
    320320<a name="l00455"></a>00455  
    321 <a name="l00457"></a><a class="code" href="classmlnorm.html#2732ae47835dd25d5784bf08fde0a546">00457</a>         vec&amp; <a class="code" href="classmlnorm.html#2732ae47835dd25d5784bf08fde0a546" title="access function">_mu_const</a>() {<span class="keywordflow">return</span> mu_const;} 
    322 <a name="l00459"></a><a class="code" href="classmlnorm.html#65ec3840c21b21102896bfd2282b47b3">00459</a>         mat&amp; <a class="code" href="classmlnorm.html#65ec3840c21b21102896bfd2282b47b3" title="access function">_A</a>() {<span class="keywordflow">return</span> A;} 
    323 <a name="l00461"></a><a class="code" href="classmlnorm.html#3ff2b03fbb5e1133a5fe1bf831939f63">00461</a>         mat <a class="code" href="classmlnorm.html#3ff2b03fbb5e1133a5fe1bf831939f63" title="access function">_R</a>() {<span class="keywordflow">return</span> <a class="code" href="classepdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a>._R().to_mat();} 
     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();} 
    324324<a name="l00462"></a>00462  
    325325<a name="l00463"></a>00463         <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_M&gt; 
    … …  
    327327<a name="l00465"></a>00465 }; 
    328328<a name="l00466"></a>00466  
    329 <a name="l00469"></a><a class="code" href="classmlstudent.html">00469</a> <span class="keyword">class </span><a class="code" href="classmlstudent.html">mlstudent</a> : <span class="keyword">public</span> <a class="code" href="classmlnorm.html" title="Normal distributed linear function with linear function of mean value;.">mlnorm</a>&lt;ldmat&gt; { 
     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; { 
    330330<a name="l00470"></a>00470 <span class="keyword">protected</span>: 
    331331<a name="l00471"></a>00471         <a class="code" href="classldmat.html" title="Matrix stored in LD form, (commonly known as UD).">ldmat</a> Lambda; 
    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="classmlnorm.html#3ff2b03fbb5e1133a5fe1bf831939f63" title="access function">_R</a>; 
     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>; 
    333333<a name="l00473"></a>00473         <a class="code" href="classldmat.html" title="Matrix stored in LD form, (commonly known as UD).">ldmat</a> Re; 
    334334<a name="l00474"></a>00474 <span class="keyword">public</span>: 
    335 <a name="l00475"></a>00475         <a class="code" href="classmlstudent.html">mlstudent</a> ( <span class="keyword">const</span> <a class="code" href="classRV.html" title="Class representing variables, most often random variables.">RV</a> &amp;rv0, <span class="keyword">const</span> <a class="code" href="classRV.html" title="Class representing variables, most often random variables.">RV</a> &amp;rvc0 ) :<a class="code" href="classmlnorm.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="classRV.html#f5c7b8bd589eef09ccdf3329a0addea0" title="Return number of scalars in the RV.">count</a>() ), 
    337 <a name="l00477"></a>00477                         <a class="code" href="classmlnorm.html#3ff2b03fbb5e1133a5fe1bf831939f63" title="access function">_R</a> ( <a class="code" href="classepdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a>._R() ) {} 
     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() ) {} 
    338338<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) { 
    339 <a name="l00479"></a>00479                 <a class="code" href="classepdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a>.set_parameters ( zeros ( <a class="code" href="classmpdf.html#f6687c07ff07d47812dd565368ca59eb" title="modeled random variable">rv</a>.<a class="code" href="classRV.html#f5c7b8bd589eef09ccdf3329a0addea0" title="Return number of scalars in the RV.">count</a>() ),Lambda ); 
     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 ); 
    340340<a name="l00480"></a>00480                 A = A0; 
    341341<a name="l00481"></a>00481                 mu_const = mu0; 
    … …  
    343343<a name="l00483"></a>00483                 Lambda = Lambda0; 
    344344<a name="l00484"></a>00484         } 
    345 <a name="l00485"></a><a class="code" href="classmlstudent.html#d153460ae0180f4bc7f28301f5cde876">00485</a>         <span class="keywordtype">void</span> <a class="code" href="classmlstudent.html#d153460ae0180f4bc7f28301f5cde876" 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 ) { 
     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 ) { 
    346346<a name="l00486"></a>00486                 _mu = A*cond + mu_const; 
    347347<a name="l00487"></a>00487                 <span class="keywordtype">double</span> zeta; 
    348348<a name="l00488"></a>00488                 <span class="comment">//ugly hack!</span> 
    349349<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>()){ 
    350 <a name="l00490"></a>00490                         zeta = Lambda.<a class="code" href="classldmat.html#d876c5f83e02b3e809b35c9de5068f14" title="Evaluates quadratic form ;.">invqform</a> ( <a class="code" href="group__core.html#g33c114e83980d883c5b211c47d5322a4" title="Concat two random variables.">concat</a>(cond, vec_1(1.0)) ); 
     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)) ); 
    351351<a name="l00491"></a>00491                 } <span class="keywordflow">else</span> { 
    352352<a name="l00492"></a>00492                         zeta = Lambda.<a class="code" href="classldmat.html#d876c5f83e02b3e809b35c9de5068f14" title="Evaluates quadratic form ;.">invqform</a> ( cond ); 
    353353<a name="l00493"></a>00493                 } 
    354 <a name="l00494"></a>00494                 <a class="code" href="classmlnorm.html#3ff2b03fbb5e1133a5fe1bf831939f63" title="access function">_R</a> = Re; 
    355 <a name="l00495"></a>00495                 <a class="code" href="classmlnorm.html#3ff2b03fbb5e1133a5fe1bf831939f63" title="access function">_R</a>*=( 1+zeta );<span class="comment">// / ( nu ); &lt;&lt; nu is in Re!!!!!!</span> 
     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> 
    356356<a name="l00496"></a>00496         }; 
    357357<a name="l00497"></a>00497  
    358358<a name="l00498"></a>00498 }; 
    359 <a name="l00508"></a><a class="code" href="classmgamma.html">00508</a> <span class="keyword">class </span><a class="code" href="classmgamma.html" title="Gamma random walk.">mgamma</a> : <span class="keyword">public</span> <a class="code" href="classmEF.html" title="Exponential family model.">mEF</a> { 
     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> { 
    360360<a name="l00509"></a>00509 <span class="keyword">protected</span>: 
    361 <a name="l00511"></a><a class="code" href="classmgamma.html#612dbf35c770a780027619aaac2c443e">00511</a>         <a class="code" href="classegamma.html" title="Gamma posterior density.">egamma</a> <a class="code" href="classepdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a>; 
    362 <a name="l00513"></a><a class="code" href="classmgamma.html#43f733cce0245a52363d566099add687">00513</a>         <span class="keywordtype">double</span> <a class="code" href="classmgamma.html#43f733cce0245a52363d566099add687" title="Constant .">k</a>; 
    363 <a name="l00515"></a><a class="code" href="classmgamma.html#5e90652837448bcc29707e7412f99691">00515</a>         vec* <a class="code" href="classmgamma.html#5e90652837448bcc29707e7412f99691" title="cache of epdf.beta">_beta</a>; 
     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>; 
    364364<a name="l00516"></a>00516  
    365365<a name="l00517"></a>00517 <span class="keyword">public</span>: 
    366 <a name="l00519"></a><a class="code" href="classmgamma.html#af43e61b86900c0398d5c0ffc83b94e6">00519</a>         <a class="code" href="classmgamma.html#af43e61b86900c0398d5c0ffc83b94e6" title="Constructor.">mgamma</a> ( <span class="keyword">const</span> <a class="code" href="classRV.html" title="Class representing variables, most often random variables.">RV</a> &amp;<a class="code" href="classmpdf.html#f6687c07ff07d47812dd565368ca59eb" title="modeled random variable">rv</a>,<span class="keyword">const</span> <a class="code" href="classRV.html" title="Class representing variables, most often random variables.">RV</a> &amp;<a class="code" href="classmpdf.html#acb7dda792b3cd5576f39fa3129abbab" title="random variable in condition">rvc</a> ): <a class="code" href="classmEF.html" title="Exponential family model.">mEF</a> ( rv,rvc ), <a class="code" href="classepdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a> ( rv ) {vec* tmp; <a class="code" href="classepdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a>._param ( tmp,<a class="code" href="classmgamma.html#5e90652837448bcc29707e7412f99691" title="cache of epdf.beta">_beta</a> );<a class="code" href="classmpdf.html#7aa894208a32f3487827df6d5054424c" title="pointer to internal epdf">ep</a>=&amp;<a class="code" href="classepdf.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="classmgamma.html#a9d646cf758a70126dde7c48790b6e94" title="Set value of k.">set_parameters</a> ( <span class="keywordtype">double</span> <a class="code" href="classmgamma.html#43f733cce0245a52363d566099add687" title="Constant .">k</a> ); 
    368 <a name="l00522"></a><a class="code" href="classmgamma.html#a61094c9f7a2d64ea77b130cbc031f97">00522</a>         <span class="keywordtype">void</span> <a class="code" href="classmgamma.html#a61094c9f7a2d64ea77b130cbc031f97" 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="classmgamma.html#5e90652837448bcc29707e7412f99691" title="cache of epdf.beta">_beta</a>=<a class="code" href="classmgamma.html#43f733cce0245a52363d566099add687" title="Constant .">k</a>/val;}; 
     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;}; 
    369369<a name="l00523"></a>00523 }; 
    370370<a name="l00524"></a>00524  
    371 <a name="l00534"></a><a class="code" href="classmigamma.html">00534</a> <span class="keyword">class </span><a class="code" href="classmigamma.html" title="Inverse-Gamma random walk.">migamma</a> : <span class="keyword">public</span> <a class="code" href="classmEF.html" title="Exponential family model.">mEF</a> { 
     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> { 
    372372<a name="l00535"></a>00535         <span class="keyword">protected</span>: 
    373 <a name="l00537"></a><a class="code" href="classmigamma.html#74712a98f587efdf35da540f7f5b5d0d">00537</a>                 <a class="code" href="classeigamma.html" title="Inverse-Gamma posterior density.">eigamma</a> <a class="code" href="classepdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a>; 
    374 <a name="l00539"></a><a class="code" href="classmigamma.html#8425bc642c6f7876b578e666c841fa9c">00539</a>                 <span class="keywordtype">double</span> <a class="code" href="classmigamma.html#8425bc642c6f7876b578e666c841fa9c" title="Constant .">k</a>; 
    375 <a name="l00541"></a><a class="code" href="classmigamma.html#92c2e81705d8edb58181b61af75574e0">00541</a>                 vec* <a class="code" href="classmigamma.html#92c2e81705d8edb58181b61af75574e0" title="cache of epdf.beta">_beta</a>; 
    376 <a name="l00543"></a><a class="code" href="classmigamma.html#fb9bf89eb2c15fc267c97eef2218ebfa">00543</a>                 vec* <a class="code" href="classmigamma.html#fb9bf89eb2c15fc267c97eef2218ebfa" title="chaceh of epdf.alpha">_alpha</a>; 
     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>; 
    377377<a name="l00544"></a>00544  
    378378<a name="l00545"></a>00545         <span class="keyword">public</span>: 
    379 <a name="l00547"></a><a class="code" href="classmigamma.html#81d6f9fe46acec656ccde245220b7090">00547</a>                 <a class="code" href="classmigamma.html#81d6f9fe46acec656ccde245220b7090" title="Constructor.">migamma</a> ( <span class="keyword">const</span> <a class="code" href="classRV.html" title="Class representing variables, most often random variables.">RV</a> &amp;<a class="code" href="classmpdf.html#f6687c07ff07d47812dd565368ca59eb" title="modeled random variable">rv</a>,<span class="keyword">const</span> <a class="code" href="classRV.html" title="Class representing variables, most often random variables.">RV</a> &amp;<a class="code" href="classmpdf.html#acb7dda792b3cd5576f39fa3129abbab" title="random variable in condition">rvc</a> ): <a class="code" href="classmEF.html" title="Exponential family model.">mEF</a> ( rv,rvc ), <a class="code" href="classepdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a> ( rv ) {<a class="code" href="classepdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a>._param ( <a class="code" href="classmigamma.html#fb9bf89eb2c15fc267c97eef2218ebfa" title="chaceh of epdf.alpha">_alpha</a>,<a class="code" href="classmigamma.html#92c2e81705d8edb58181b61af75574e0" title="cache of epdf.beta">_beta</a> );<a class="code" href="classmpdf.html#7aa894208a32f3487827df6d5054424c" title="pointer to internal epdf">ep</a>=&amp;<a class="code" href="classepdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a>;}; 
    380 <a name="l00549"></a><a class="code" href="classmigamma.html#6cf801c0319ffcfc6317e9f2ecef4cf8">00549</a>                 <span class="keywordtype">void</span> <a class="code" href="classmigamma.html#6cf801c0319ffcfc6317e9f2ecef4cf8" title="Set value of k.">set_parameters</a> ( <span class="keywordtype">double</span> k0 ){<a class="code" href="classmigamma.html#8425bc642c6f7876b578e666c841fa9c" title="Constant .">k</a>=k0;*<a class="code" href="classmigamma.html#fb9bf89eb2c15fc267c97eef2218ebfa" title="chaceh of epdf.alpha">_alpha</a>=1.0/(<a class="code" href="classmigamma.html#8425bc642c6f7876b578e666c841fa9c" title="Constant .">k</a>*<a class="code" href="classmigamma.html#8425bc642c6f7876b578e666c841fa9c" title="Constant .">k</a>)+2;}; 
    381 <a name="l00550"></a><a class="code" href="classmigamma.html#739c196dfcc586dec49043150da6ed0d">00550</a>                 <span class="keywordtype">void</span> <a class="code" href="classmigamma.html#739c196dfcc586dec49043150da6ed0d" 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="classmigamma.html#92c2e81705d8edb58181b61af75574e0" title="cache of epdf.beta">_beta</a>=elem_mult(val,(*<a class="code" href="classmigamma.html#fb9bf89eb2c15fc267c97eef2218ebfa" title="chaceh of epdf.alpha">_alpha</a>-1)); 
     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)); 
    383383<a name="l00552"></a>00552                 }; 
    384384<a name="l00553"></a>00553 }; 
    385385<a name="l00554"></a>00554  
    386 <a name="l00566"></a><a class="code" href="classmgamma__fix.html">00566</a> <span class="keyword">class </span><a class="code" href="classmgamma__fix.html" title="Gamma random walk around a fixed point.">mgamma_fix</a> : <span class="keyword">public</span> <a class="code" href="classmgamma.html" title="Gamma random walk.">mgamma</a> { 
     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> { 
    387387<a name="l00567"></a>00567 <span class="keyword">protected</span>: 
    388 <a name="l00569"></a><a class="code" href="classmgamma__fix.html#3f48c09caddc298901ad75fe7c0529f6">00569</a>         <span class="keywordtype">double</span> <a class="code" href="classmgamma__fix.html#3f48c09caddc298901ad75fe7c0529f6" title="parameter l">l</a>; 
    389 <a name="l00571"></a><a class="code" href="classmgamma__fix.html#81ce49029ecc385418619b200dcafeb0">00571</a>         vec <a class="code" href="classmgamma__fix.html#81ce49029ecc385418619b200dcafeb0" title="reference vector">refl</a>; 
     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>; 
    390390<a name="l00572"></a>00572 <span class="keyword">public</span>: 
    391 <a name="l00574"></a><a class="code" href="classmgamma__fix.html#b92c3d2e5fd0381033a072e5ef3bcf80">00574</a>         <a class="code" href="classmgamma__fix.html#b92c3d2e5fd0381033a072e5ef3bcf80" title="Constructor.">mgamma_fix</a> ( <span class="keyword">const</span> <a class="code" href="classRV.html" title="Class representing variables, most often random variables.">RV</a> &amp;<a class="code" href="classmpdf.html#f6687c07ff07d47812dd565368ca59eb" title="modeled random variable">rv</a>,<span class="keyword">const</span> <a class="code" href="classRV.html" title="Class representing variables, most often random variables.">RV</a> &amp;<a class="code" href="classmpdf.html#acb7dda792b3cd5576f39fa3129abbab" title="random variable in condition">rvc</a> ) : <a class="code" href="classmgamma.html" title="Gamma random walk.">mgamma</a> ( rv,rvc ),<a class="code" href="classmgamma__fix.html#81ce49029ecc385418619b200dcafeb0" title="reference vector">refl</a> ( rv.count() ) {}; 
    392 <a name="l00576"></a><a class="code" href="classmgamma__fix.html#ec6f846896749e27cb7be9fa48dd1cb1">00576</a>         <span class="keywordtype">void</span> <a class="code" href="classmgamma__fix.html#ec6f846896749e27cb7be9fa48dd1cb1" 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="classmgamma.html#a9d646cf758a70126dde7c48790b6e94" title="Set value of k.">mgamma::set_parameters</a> ( k0 ); 
    394 <a name="l00578"></a>00578                 <a class="code" href="classmgamma__fix.html#81ce49029ecc385418619b200dcafeb0" title="reference vector">refl</a>=pow ( ref0,1.0-l0 );<a class="code" href="classmgamma__fix.html#3f48c09caddc298901ad75fe7c0529f6" title="parameter l">l</a>=l0; 
     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; 
    395395<a name="l00579"></a>00579         }; 
    396396<a name="l00580"></a>00580  
    397 <a name="l00581"></a><a class="code" href="classmgamma__fix.html#6ea3931eec7b7da7b693e45981052460">00581</a>         <span class="keywordtype">void</span> <a class="code" href="classmgamma__fix.html#6ea3931eec7b7da7b693e45981052460" 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="classmgamma__fix.html#81ce49029ecc385418619b200dcafeb0" title="reference vector">refl</a>,pow ( val,<a class="code" href="classmgamma__fix.html#3f48c09caddc298901ad75fe7c0529f6" title="parameter l">l</a> ) ); *<a class="code" href="classmgamma.html#5e90652837448bcc29707e7412f99691" title="cache of epdf.beta">_beta</a>=<a class="code" href="classmgamma.html#43f733cce0245a52363d566099add687" title="Constant .">k</a>/mean;}; 
     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;}; 
    398398<a name="l00582"></a>00582 }; 
    399399<a name="l00583"></a>00583  
    400400<a name="l00584"></a>00584  
    401 <a name="l00597"></a><a class="code" href="classmigamma__fix.html">00597</a> <span class="keyword">class </span><a class="code" href="classmigamma__fix.html" title="Inverse-Gamma random walk around a fixed point.">migamma_fix</a> : <span class="keyword">public</span> <a class="code" href="classmigamma.html" title="Inverse-Gamma random walk.">migamma</a> { 
     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> { 
    402402<a name="l00598"></a>00598         <span class="keyword">protected</span>: 
    403 <a name="l00600"></a><a class="code" href="classmigamma__fix.html#13e0b9e3faf370a5ac24f2d8534047ec">00600</a>                 <span class="keywordtype">double</span> <a class="code" href="classmigamma__fix.html#13e0b9e3faf370a5ac24f2d8534047ec" title="parameter l">l</a>; 
    404 <a name="l00602"></a><a class="code" href="classmigamma__fix.html#7d0576daba2a1de5dc040dbfbd7dd446">00602</a>                 vec <a class="code" href="classmigamma__fix.html#7d0576daba2a1de5dc040dbfbd7dd446" title="reference vector">refl</a>; 
     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>; 
    405405<a name="l00603"></a>00603         <span class="keyword">public</span>: 
    406 <a name="l00605"></a><a class="code" href="classmigamma__fix.html#85ff4fae4d3faefed060c515f255207e">00605</a>                 <a class="code" href="classmigamma__fix.html#85ff4fae4d3faefed060c515f255207e" title="Constructor.">migamma_fix</a> ( <span class="keyword">const</span> <a class="code" href="classRV.html" title="Class representing variables, most often random variables.">RV</a> &amp;<a class="code" href="classmpdf.html#f6687c07ff07d47812dd565368ca59eb" title="modeled random variable">rv</a>,<span class="keyword">const</span> <a class="code" href="classRV.html" title="Class representing variables, most often random variables.">RV</a> &amp;<a class="code" href="classmpdf.html#acb7dda792b3cd5576f39fa3129abbab" title="random variable in condition">rvc</a> ) : <a class="code" href="classmigamma.html" title="Inverse-Gamma random walk.">migamma</a> ( rv,rvc ),<a class="code" href="classmigamma__fix.html#7d0576daba2a1de5dc040dbfbd7dd446" title="reference vector">refl</a> ( rv.count() ) {}; 
    407 <a name="l00607"></a><a class="code" href="classmigamma__fix.html#6266e14eb59fe36f494cfb5934a8e987">00607</a>                 <span class="keywordtype">void</span> <a class="code" href="classmigamma__fix.html#6266e14eb59fe36f494cfb5934a8e987" 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="classmigamma.html#6cf801c0319ffcfc6317e9f2ecef4cf8" title="Set value of k.">migamma::set_parameters</a> ( k0 ); 
    409 <a name="l00609"></a>00609                         <a class="code" href="classmigamma__fix.html#7d0576daba2a1de5dc040dbfbd7dd446" title="reference vector">refl</a>=pow ( ref0,1.0-l0 );<a class="code" href="classmigamma__fix.html#13e0b9e3faf370a5ac24f2d8534047ec" title="parameter l">l</a>=l0; 
     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; 
    410410<a name="l00610"></a>00610                 }; 
    411411<a name="l00611"></a>00611  
    412 <a name="l00612"></a><a class="code" href="classmigamma__fix.html#a69739eebfe05835db11bc3544cec6a1">00612</a>                 <span class="keywordtype">void</span> <a class="code" href="classmigamma__fix.html#a69739eebfe05835db11bc3544cec6a1" 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="classmigamma__fix.html#7d0576daba2a1de5dc040dbfbd7dd446" title="reference vector">refl</a>,pow ( val,<a class="code" href="classmigamma__fix.html#13e0b9e3faf370a5ac24f2d8534047ec" title="parameter l">l</a> ) ); <a class="code" href="classmigamma.html#739c196dfcc586dec49043150da6ed0d" title="Update ep so that it represents this mpdf conditioned on rvc = cond.">migamma::condition</a>(mean);}; 
     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);}; 
    413413<a name="l00613"></a>00613 }; 
    414 <a name="l00615"></a><a class="code" href="libEF_8h.html#99497a3ff630f761cf6bff7babd23212">00615</a> <span class="keyword">enum</span> <a class="code" href="libEF_8h.html#99497a3ff630f761cf6bff7babd23212" title="Switch between various resampling methods.">RESAMPLING_METHOD</a> { MULTINOMIAL = 0, STRATIFIED = 1, SYSTEMATIC = 3 }; 
    415 <a name="l00621"></a><a class="code" href="classeEmp.html">00621</a> <span class="keyword">class </span><a class="code" href="classeEmp.html" title="Weighted empirical density.">eEmp</a>: <span class="keyword">public</span> <a class="code" href="classepdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a> { 
     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> { 
    416416<a name="l00622"></a>00622 <span class="keyword">protected</span> : 
    417 <a name="l00624"></a><a class="code" href="classeEmp.html#8c33034de0e35f03f8bb85d3d67438fd">00624</a>         <span class="keywordtype">int</span> <a class="code" href="classeEmp.html#8c33034de0e35f03f8bb85d3d67438fd" title="Number of particles.">n</a>; 
    418 <a name="l00626"></a><a class="code" href="classeEmp.html#ae78d144404ddba843c93b171b215de8">00626</a>         vec <a class="code" href="classeEmp.html#ae78d144404ddba843c93b171b215de8" title="Sample weights .">w</a>; 
    419 <a name="l00628"></a><a class="code" href="classeEmp.html#a4d6f4bbd6a6824fc39f14676701279a">00628</a>         Array&lt;vec&gt; <a class="code" href="classeEmp.html#a4d6f4bbd6a6824fc39f14676701279a" title="Samples .">samples</a>; 
     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>; 
    420420<a name="l00629"></a>00629 <span class="keyword">public</span>: 
    421 <a name="l00631"></a><a class="code" href="classeEmp.html#0c04b073ecd0dae3d498e680ae27e9e4">00631</a>         <a class="code" href="classeEmp.html#0c04b073ecd0dae3d498e680ae27e9e4" title="Default constructor.">eEmp</a> ( <span class="keyword">const</span> <a class="code" href="classRV.html" title="Class representing variables, most often random variables.">RV</a> &amp;rv0 ,<span class="keywordtype">int</span> n0 ) :<a class="code" href="classepdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a> ( rv0 ),<a class="code" href="classeEmp.html#8c33034de0e35f03f8bb85d3d67438fd" title="Number of particles.">n</a> ( n0 ),<a class="code" href="classeEmp.html#ae78d144404ddba843c93b171b215de8" title="Sample weights .">w</a> ( <a class="code" href="classeEmp.html#8c33034de0e35f03f8bb85d3d67438fd" title="Number of particles.">n</a> ),<a class="code" href="classeEmp.html#a4d6f4bbd6a6824fc39f14676701279a" title="Samples .">samples</a> ( <a class="code" href="classeEmp.html#8c33034de0e35f03f8bb85d3d67438fd" title="Number of particles.">n</a> ) {}; 
    422 <a name="l00633"></a>00633         <span class="keywordtype">void</span> <a class="code" href="classeEmp.html#eab03bd3381aaea11ce34d5a26556353" 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="classepdf.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="classeEmp.html#e31bc9e6196173c3480b06a761a3e716" title="Set sample.">set_samples</a> ( <span class="keyword">const</span> <a class="code" href="classepdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a>* pdf0 ); 
    424 <a name="l00637"></a><a class="code" href="classeEmp.html#a4215f6a5a04d07b43f7ebaa942e15f1">00637</a>         <span class="keywordtype">void</span> <a class="code" href="classeEmp.html#a4215f6a5a04d07b43f7ebaa942e15f1" 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="classeEmp.html#ae78d144404ddba843c93b171b215de8" title="Sample weights .">w</a>.set_size(n0,copy);<a class="code" href="classeEmp.html#a4d6f4bbd6a6824fc39f14676701279a" title="Samples .">samples</a>.set_size(n0,copy);}; 
    425 <a name="l00639"></a><a class="code" href="classeEmp.html#31b2bfb73b72486a5c89f2ab850c7a9b">00639</a>         vec&amp; <a class="code" href="classeEmp.html#31b2bfb73b72486a5c89f2ab850c7a9b" title="Potentially dangerous, use with care.">_w</a>()  {<span class="keywordflow">return</span> <a class="code" href="classeEmp.html#ae78d144404ddba843c93b171b215de8" title="Sample weights .">w</a>;}; 
    426 <a name="l00641"></a><a class="code" href="classeEmp.html#d6f4ae1a67ecd2bff8b9f176ee261afc">00641</a>         <span class="keyword">const</span> vec&amp; <a class="code" href="classeEmp.html#31b2bfb73b72486a5c89f2ab850c7a9b" title="Potentially dangerous, use with care.">_w</a>()<span class="keyword"> const </span>{<span class="keywordflow">return</span> <a class="code" href="classeEmp.html#ae78d144404ddba843c93b171b215de8" title="Sample weights .">w</a>;}; 
    427 <a name="l00643"></a><a class="code" href="classeEmp.html#31b747eca73b16f30370827ba4cc3575">00643</a>         Array&lt;vec&gt;&amp; <a class="code" href="classeEmp.html#31b747eca73b16f30370827ba4cc3575" title="access function">_samples</a>() {<span class="keywordflow">return</span> <a class="code" href="classeEmp.html#a4d6f4bbd6a6824fc39f14676701279a" title="Samples .">samples</a>;}; 
    428 <a name="l00645"></a><a class="code" href="classeEmp.html#bd48c1c36e2e9e78dbcea7df66dcbf25">00645</a>         <span class="keyword">const</span> Array&lt;vec&gt;&amp; <a class="code" href="classeEmp.html#31b747eca73b16f30370827ba4cc3575" title="access function">_samples</a>()<span class="keyword"> const </span>{<span class="keywordflow">return</span> <a class="code" href="classeEmp.html#a4d6f4bbd6a6824fc39f14676701279a" title="Samples .">samples</a>;}; 
    429 <a name="l00647"></a>00647         ivec <a class="code" href="classeEmp.html#77268292fc4465cb73ddbfb1f2932a59" title="Function performs resampling, i.e. removal of low-weight samples and duplication...">resample</a> ( <a class="code" href="libEF_8h.html#99497a3ff630f761cf6bff7babd23212" title="Switch between various resampling methods.">RESAMPLING_METHOD</a> method = SYSTEMATIC ); 
    430 <a name="l00649"></a><a class="code" href="classeEmp.html#83f9283f92b805508d896479dc1ccf12">00649</a>         vec <a class="code" href="classeEmp.html#83f9283f92b805508d896479dc1ccf12" 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="classeEmp.html#884f16c9fc1f888408686a660a95dacd">00651</a>         <span class="keywordtype">double</span> <a class="code" href="classeEmp.html#884f16c9fc1f888408686a660a95dacd" 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="classeEmp.html#ba055c19038cc72628d98e25197e982d">00652</a>         vec <a class="code" href="classeEmp.html#ba055c19038cc72628d98e25197e982d" title="return expected value">mean</a>()<span class="keyword"> const </span>{ 
    433 <a name="l00653"></a>00653                 vec pom=zeros ( <a class="code" href="classepdf.html#74da992e3f5d598da8850b646b79b9d9" title="Identified of the random variable.">rv</a>.<a class="code" href="classRV.html#f5c7b8bd589eef09ccdf3329a0addea0" 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="classeEmp.html#8c33034de0e35f03f8bb85d3d67438fd" title="Number of particles.">n</a>;i++ ) {pom+=<a class="code" href="classeEmp.html#a4d6f4bbd6a6824fc39f14676701279a" title="Samples .">samples</a> ( i ) *<a class="code" href="classeEmp.html#ae78d144404ddba843c93b171b215de8" title="Sample weights .">w</a> ( i );} 
     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 );} 
    435435<a name="l00655"></a>00655                 <span class="keywordflow">return</span> pom; 
    436436<a name="l00656"></a>00656         } 
    437 <a name="l00657"></a><a class="code" href="classeEmp.html#738f6ec90cf1a8306ed8c259142a2f79">00657</a>         vec <a class="code" href="classeEmp.html#738f6ec90cf1a8306ed8c259142a2f79" 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="classepdf.html#74da992e3f5d598da8850b646b79b9d9" title="Identified of the random variable.">rv</a>.<a class="code" href="classRV.html#f5c7b8bd589eef09ccdf3329a0addea0" 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="classeEmp.html#8c33034de0e35f03f8bb85d3d67438fd" title="Number of particles.">n</a>;i++ ) {pom+=pow(<a class="code" href="classeEmp.html#a4d6f4bbd6a6824fc39f14676701279a" title="Samples .">samples</a> ( i ),2) *<a class="code" href="classeEmp.html#ae78d144404ddba843c93b171b215de8" title="Sample weights .">w</a> ( i );} 
    440 <a name="l00660"></a>00660                 <span class="keywordflow">return</span> pom-pow(<a class="code" href="classeEmp.html#ba055c19038cc72628d98e25197e982d" title="return expected value">mean</a>(),2); 
     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); 
    441441<a name="l00661"></a>00661         } 
    442442<a name="l00662"></a>00662 }; 
    … …  
    445445<a name="l00666"></a>00666  
    446446<a name="l00667"></a>00667 <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt; 
    447 <a name="l00668"></a><a class="code" href="classenorm.html#0caf54fed9e48f9fe28b534b2027df2f">00668</a> <a class="code" href="classenorm.html#0caf54fed9e48f9fe28b534b2027df2f" title="Default constructor.">enorm&lt;sq_T&gt;::enorm</a> ( <span class="keyword">const</span> <a class="code" href="classRV.html" title="Class representing variables, most often random variables.">RV</a> &amp;rv ) :<a class="code" href="classeEF.html" title="General conjugate exponential family posterior density.">eEF</a> ( rv ), mu ( rv.count() ),R ( rv.count() ),dim ( rv.count() ) {}; 
     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() ) {}; 
    448448<a name="l00669"></a>00669  
    449449<a name="l00670"></a>00670 <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt; 
    450 <a name="l00671"></a><a class="code" href="classenorm.html#1394a65caa6e00d42e00cc99b12227af">00671</a> <span class="keywordtype">void</span> <a class="code" href="classenorm.html#1394a65caa6e00d42e00cc99b12227af" title="Set mean value mu and covariance R.">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 ) { 
     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 ) { 
    451451<a name="l00672"></a>00672 <span class="comment">//Fixme test dimensions of mu0 and R0;</span> 
    452 <a name="l00673"></a>00673         <a class="code" href="classenorm.html#71fde0d54bba147e00f612577f95ad20" title="mean value">mu</a> = mu0; 
    453 <a name="l00674"></a>00674         <a class="code" href="classenorm.html#4ccc8d8514d644ef1c98d8ab023748a1" title="Covariance matrix in decomposed form.">R</a> = R0; 
     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; 
    454454<a name="l00675"></a>00675 }; 
    455455<a name="l00676"></a>00676  
    456456<a name="l00677"></a>00677 <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt; 
    457 <a name="l00678"></a><a class="code" href="classenorm.html#5bf185e31e5954fceb90ada3debd2ff2">00678</a> <span class="keywordtype">void</span> <a class="code" href="classenorm.html#5bf185e31e5954fceb90ada3debd2ff2" title="dupdate in exponential form (not really handy)">enorm&lt;sq_T&gt;::dupdate</a> ( mat &amp;v, <span class="keywordtype">double</span> nu ) { 
     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 ) { 
    458458<a name="l00679"></a>00679         <span class="comment">//</span> 
    459459<a name="l00680"></a>00680 }; 
    … …  
    465465<a name="l00686"></a>00686  
    466466<a name="l00687"></a>00687 <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt; 
    467 <a name="l00688"></a><a class="code" href="classenorm.html#60b47544f6181ffd4530d3e415ce12c5">00688</a> vec <a class="code" href="classenorm.html#60b47544f6181ffd4530d3e415ce12c5" title="Returns a sample,  from density .">enorm&lt;sq_T&gt;::sample</a>()<span class="keyword"> const </span>{ 
    468 <a name="l00689"></a>00689         vec x ( <a class="code" href="classenorm.html#6938fc390a19cdaf6ad4503fcbaada4e" title="dimension (redundant from rv.count() for easier coding )">dim</a> ); 
    469 <a name="l00690"></a>00690         NorRNG.sample_vector ( <a class="code" href="classenorm.html#6938fc390a19cdaf6ad4503fcbaada4e" title="dimension (redundant from rv.count() for easier coding )">dim</a>,x ); 
    470 <a name="l00691"></a>00691         vec smp = <a class="code" href="classenorm.html#4ccc8d8514d644ef1c98d8ab023748a1" title="Covariance matrix in decomposed form.">R</a>.sqrt_mult ( x ); 
    471 <a name="l00692"></a>00692  
    472 <a name="l00693"></a>00693         smp += <a class="code" href="classenorm.html#71fde0d54bba147e00f612577f95ad20" title="mean value">mu</a>; 
    473 <a name="l00694"></a>00694         <span class="keywordflow">return</span> smp; 
    474 <a name="l00695"></a>00695 }; 
    475 <a name="l00696"></a>00696  
    476 <a name="l00697"></a>00697 <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt; 
    477 <a name="l00698"></a><a class="code" href="classenorm.html#60f0f3bfa53d6e65843eea9532b16d36">00698</a> mat <a class="code" href="classenorm.html#60b47544f6181ffd4530d3e415ce12c5" title="Returns a sample,  from density .">enorm&lt;sq_T&gt;::sample</a> ( <span class="keywordtype">int</span> N )<span class="keyword"> const </span>{ 
    478 <a name="l00699"></a>00699         mat X ( <a class="code" href="classenorm.html#6938fc390a19cdaf6ad4503fcbaada4e" title="dimension (redundant from rv.count() for easier coding )">dim</a>,N ); 
    479 <a name="l00700"></a>00700         vec x ( <a class="code" href="classenorm.html#6938fc390a19cdaf6ad4503fcbaada4e" title="dimension (redundant from rv.count() for easier coding )">dim</a> ); 
    480 <a name="l00701"></a>00701         vec pom; 
    481 <a name="l00702"></a>00702         <span class="keywordtype">int</span> i; 
    482 <a name="l00703"></a>00703  
    483 <a name="l00704"></a>00704         <span class="keywordflow">for</span> ( i=0;i&lt;N;i++ ) { 
    484 <a name="l00705"></a>00705                 NorRNG.sample_vector ( <a class="code" href="classenorm.html#6938fc390a19cdaf6ad4503fcbaada4e" title="dimension (redundant from rv.count() for easier coding )">dim</a>,x ); 
    485 <a name="l00706"></a>00706                 pom = <a class="code" href="classenorm.html#4ccc8d8514d644ef1c98d8ab023748a1" title="Covariance matrix in decomposed form.">R</a>.sqrt_mult ( x ); 
    486 <a name="l00707"></a>00707                 pom +=<a class="code" href="classenorm.html#71fde0d54bba147e00f612577f95ad20" title="mean value">mu</a>; 
    487 <a name="l00708"></a>00708                 X.set_col ( i, pom ); 
    488 <a name="l00709"></a>00709         } 
    489 <a name="l00710"></a>00710  
    490 <a name="l00711"></a>00711         <span class="keywordflow">return</span> X; 
    491 <a name="l00712"></a>00712 }; 
    492 <a name="l00713"></a>00713  
    493 <a name="l00714"></a>00714 <span class="comment">// template&lt;class sq_T&gt;</span> 
    494 <a name="l00715"></a>00715 <span class="comment">// double enorm&lt;sq_T&gt;::eval ( const vec &amp;val ) const {</span> 
    495 <a name="l00716"></a>00716 <span class="comment">//      double pdfl,e;</span> 
    496 <a name="l00717"></a>00717 <span class="comment">//      pdfl = evallog ( val );</span> 
    497 <a name="l00718"></a>00718 <span class="comment">//      e = exp ( pdfl );</span> 
    498 <a name="l00719"></a>00719 <span class="comment">//      return e;</span> 
    499 <a name="l00720"></a>00720 <span class="comment">// };</span> 
    500 <a name="l00721"></a>00721  
    501 <a name="l00722"></a>00722 <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt; 
    502 <a name="l00723"></a><a class="code" href="classenorm.html#50cb0a083d97a7adbbd97c92e712c46c">00723</a> <span class="keywordtype">double</span> <a class="code" href="classenorm.html#50cb0a083d97a7adbbd97c92e712c46c" title="Evaluate normalized log-probability.">enorm&lt;sq_T&gt;::evallog_nn</a> ( <span class="keyword">const</span> vec &amp;val )<span class="keyword"> const </span>{ 
    503 <a name="l00724"></a>00724         <span class="comment">// 1.83787706640935 = log(2pi)</span> 
    504 <a name="l00725"></a>00725         <span class="keywordtype">double</span> tmp=-0.5* ( <a class="code" href="classenorm.html#4ccc8d8514d644ef1c98d8ab023748a1" title="Covariance matrix in decomposed form.">R</a>.invqform ( <a class="code" href="classenorm.html#71fde0d54bba147e00f612577f95ad20" title="mean value">mu</a>-val ) );<span class="comment">// - lognc();</span> 
    505 <a name="l00726"></a>00726         <span class="keywordflow">return</span>  tmp; 
    506 <a name="l00727"></a>00727 }; 
    507 <a name="l00728"></a>00728  
    508 <a name="l00729"></a>00729 <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt; 
    509 <a name="l00730"></a><a class="code" href="classenorm.html#b289a36a69db59d182bb6eba9c05d4a8">00730</a> <span class="keyword">inline</span> <span class="keywordtype">double</span> <a class="code" href="classenorm.html#b289a36a69db59d182bb6eba9c05d4a8" title="logarithm of the normalizing constant, ">enorm&lt;sq_T&gt;::lognc</a> ()<span class="keyword"> const </span>{ 
    510 <a name="l00731"></a>00731         <span class="comment">// 1.83787706640935 = log(2pi)</span> 
    511 <a name="l00732"></a>00732         <span class="keywordtype">double</span> tmp=0.5* ( <a class="code" href="classenorm.html#4ccc8d8514d644ef1c98d8ab023748a1" title="Covariance matrix in decomposed form.">R</a>.cols() * 1.83787706640935 +<a class="code" href="classenorm.html#4ccc8d8514d644ef1c98d8ab023748a1" title="Covariance matrix in decomposed form.">R</a>.logdet() ); 
    512 <a name="l00733"></a>00733         <span class="keywordflow">return</span> tmp; 
    513 <a name="l00734"></a>00734 }; 
    514 <a name="l00735"></a>00735  
    515 <a name="l00736"></a>00736 <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt; 
    516 <a name="l00737"></a><a class="code" href="classmlnorm.html#3a5ad4798d8a3878c5e93b8e796c8837">00737</a> <a class="code" href="classmlnorm.html#3a5ad4798d8a3878c5e93b8e796c8837" title="Constructor.">mlnorm&lt;sq_T&gt;::mlnorm</a> ( <span class="keyword">const</span> <a class="code" href="classRV.html" title="Class representing variables, most often random variables.">RV</a> &amp;rv0, <span class="keyword">const</span> <a class="code" href="classRV.html" title="Class representing variables, most often random variables.">RV</a> &amp;rvc0 ) :<a class="code" href="classmEF.html" title="Exponential family model.">mEF</a> ( rv0,rvc0 ),<a class="code" href="classepdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a> ( rv0 ),A ( rv0.count(),rv0.count() ),<a class="code" href="classenorm.html#0b8cb284e5af920a1b64a21d057ec5ac" title="returns a pointer to the internal mean value. Use with Care!">_mu</a> ( <a class="code" href="classepdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a>.<a class="code" href="classenorm.html#0b8cb284e5af920a1b64a21d057ec5ac" title="returns a pointer to the internal mean value. Use with Care!">_mu</a>() ) { 
    517 <a name="l00738"></a>00738         <a class="code" href="classmpdf.html#7aa894208a32f3487827df6d5054424c" title="pointer to internal epdf">ep</a> =&amp;<a class="code" href="classepdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a>; 
    518 <a name="l00739"></a>00739 } 
    519 <a name="l00740"></a>00740  
    520 <a name="l00741"></a>00741 <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt; 
    521 <a name="l00742"></a><a class="code" href="classmlnorm.html#f95dfce0b500636a44ecd7e5210de999">00742</a> <span class="keywordtype">void</span> <a class="code" href="classmlnorm.html#f95dfce0b500636a44ecd7e5210de999" title="Set A and R.">mlnorm&lt;sq_T&gt;::set_parameters</a> ( <span class="keyword">const</span> mat &amp;A0, <span class="keyword">const</span> vec &amp;mu0, <span class="keyword">const</span> sq_T &amp;R0 ) { 
    522 <a name="l00743"></a>00743         <a class="code" href="classepdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a>.set_parameters ( zeros ( <a class="code" href="classmpdf.html#f6687c07ff07d47812dd565368ca59eb" title="modeled random variable">rv</a>.<a class="code" href="classRV.html#f5c7b8bd589eef09ccdf3329a0addea0" title="Return number of scalars in the RV.">count</a>() ),R0 ); 
    523 <a name="l00744"></a>00744         A = A0; 
    524 <a name="l00745"></a>00745         mu_const = mu0; 
    525 <a name="l00746"></a>00746 } 
    526 <a name="l00747"></a>00747  
    527 <a name="l00748"></a>00748 <span class="comment">// template&lt;class sq_T&gt;</span> 
    528 <a name="l00749"></a>00749 <span class="comment">// vec mlnorm&lt;sq_T&gt;::samplecond (const  vec &amp;cond, double &amp;lik ) {</span> 
    529 <a name="l00750"></a>00750 <span class="comment">//      this-&gt;condition ( cond );</span> 
    530 <a name="l00751"></a>00751 <span class="comment">//      vec smp = epdf.sample();</span> 
    531 <a name="l00752"></a>00752 <span class="comment">//      lik = epdf.eval ( smp );</span> 
    532 <a name="l00753"></a>00753 <span class="comment">//      return smp;</span> 
    533 <a name="l00754"></a>00754 <span class="comment">// }</span> 
    534 <a name="l00755"></a>00755  
    535 <a name="l00756"></a>00756 <span class="comment">// template&lt;class sq_T&gt;</span> 
    536 <a name="l00757"></a>00757 <span class="comment">// mat mlnorm&lt;sq_T&gt;::samplecond (const vec &amp;cond, vec &amp;lik, int n ) {</span> 
    537 <a name="l00758"></a>00758 <span class="comment">//      int i;</span> 
    538 <a name="l00759"></a>00759 <span class="comment">//      int dim = rv.count();</span> 
    539 <a name="l00760"></a>00760 <span class="comment">//      mat Smp ( dim,n );</span> 
    540 <a name="l00761"></a>00761 <span class="comment">//      vec smp ( dim );</span> 
    541 <a name="l00762"></a>00762 <span class="comment">//      this-&gt;condition ( cond );</span> 
    542 <a name="l00763"></a>00763 <span class="comment">//</span> 
    543 <a name="l00764"></a>00764 <span class="comment">//      for ( i=0; i&lt;n; i++ ) {</span> 
    544 <a name="l00765"></a>00765 <span class="comment">//              smp = epdf.sample();</span> 
    545 <a name="l00766"></a>00766 <span class="comment">//              lik ( i ) = epdf.eval ( smp );</span> 
    546 <a name="l00767"></a>00767 <span class="comment">//              Smp.set_col ( i ,smp );</span> 
    547 <a name="l00768"></a>00768 <span class="comment">//      }</span> 
    548 <a name="l00769"></a>00769 <span class="comment">//</span> 
    549 <a name="l00770"></a>00770 <span class="comment">//      return Smp;</span> 
    550 <a name="l00771"></a>00771 <span class="comment">// }</span> 
    551 <a name="l00772"></a>00772  
    552 <a name="l00773"></a>00773 <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt; 
    553 <a name="l00774"></a><a class="code" href="classmlnorm.html#d41126455ac64b888a38f677886e1b40">00774</a> <span class="keywordtype">void</span> <a class="code" href="classmlnorm.html#d41126455ac64b888a38f677886e1b40" title="Set value of rvc . Result of this operation is stored in epdf use function _ep to...">mlnorm&lt;sq_T&gt;::condition</a> ( <span class="keyword">const</span> vec &amp;cond ) { 
    554 <a name="l00775"></a>00775         _mu = A*cond + mu_const; 
    555 <a name="l00776"></a>00776 <span class="comment">//R is already assigned;</span> 
    556 <a name="l00777"></a>00777 } 
    557 <a name="l00778"></a>00778  
    558 <a name="l00779"></a>00779 <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt; 
    559 <a name="l00780"></a><a class="code" href="classenorm.html#af50a6102846060bcb23a670bf38117b">00780</a> <a class="code" href="classepdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a>* <a class="code" href="classenorm.html#af50a6102846060bcb23a670bf38117b" title="Return marginal density on the given RV, the remainig rvs are intergrated out.">enorm&lt;sq_T&gt;::marginal</a> ( <span class="keyword">const</span> <a class="code" href="classRV.html" title="Class representing variables, most often random variables.">RV</a> &amp;rvn )<span class="keyword"> const </span>{ 
    560 <a name="l00781"></a>00781         ivec irvn = rvn.<a class="code" href="classRV.html#bb724fa4e2d9ed7bfd0993b5975018a4">dataind</a> ( <a class="code" href="classepdf.html#74da992e3f5d598da8850b646b79b9d9" title="Identified of the random variable.">rv</a> ); 
    561 <a name="l00782"></a>00782  
    562 <a name="l00783"></a>00783         sq_T Rn ( <a class="code" href="classenorm.html#4ccc8d8514d644ef1c98d8ab023748a1" title="Covariance matrix in decomposed form.">R</a>,irvn ); 
    563 <a name="l00784"></a>00784         <a class="code" href="classenorm.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="classenorm.html" title="Gaussian density with positive definite (decomposed) covariance matrix.">enorm&lt;sq_T&gt;</a> ( rvn ); 
    564 <a name="l00785"></a>00785         tmp-&gt;<a class="code" href="classenorm.html#1394a65caa6e00d42e00cc99b12227af" title="Set mean value mu and covariance R.">set_parameters</a> ( <a class="code" href="classenorm.html#71fde0d54bba147e00f612577f95ad20" title="mean value">mu</a> ( irvn ), Rn ); 
    565 <a name="l00786"></a>00786         <span class="keywordflow">return</span> tmp; 
    566 <a name="l00787"></a>00787 } 
    567 <a name="l00788"></a>00788  
    568 <a name="l00789"></a>00789 <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt; 
    569 <a name="l00790"></a><a class="code" href="classenorm.html#921024bd6d5a0e65f2af2e39bf38dfca">00790</a> <a class="code" href="classmpdf.html" title="Conditional probability density, e.g. modeling some dependencies.">mpdf</a>* <a class="code" href="classenorm.html#921024bd6d5a0e65f2af2e39bf38dfca" title="Return conditional density on the given RV, the remaining rvs will be in conditioning...">enorm&lt;sq_T&gt;::condition</a> ( <span class="keyword">const</span> <a class="code" href="classRV.html" title="Class representing variables, most often random variables.">RV</a> &amp;rvn )<span class="keyword"> const </span>{ 
    570 <a name="l00791"></a>00791  
    571 <a name="l00792"></a>00792         <a class="code" href="classRV.html" title="Class representing variables, most often random variables.">RV</a> rvc = <a class="code" href="classepdf.html#74da992e3f5d598da8850b646b79b9d9" title="Identified of the random variable.">rv</a>.<a class="code" href="classRV.html#b9d175c327c21488b1e2fb756a84e149" title="Subtract another variable from the current one.">subt</a> ( rvn ); 
    572 <a name="l00793"></a>00793         it_assert_debug ( ( rvc.<a class="code" href="classRV.html#f5c7b8bd589eef09ccdf3329a0addea0" title="Return number of scalars in the RV.">count</a>() +rvn.<a class="code" href="classRV.html#f5c7b8bd589eef09ccdf3329a0addea0" title="Return number of scalars in the RV.">count</a>() ==<a class="code" href="classepdf.html#74da992e3f5d598da8850b646b79b9d9" title="Identified of the random variable.">rv</a>.<a class="code" href="classRV.html#f5c7b8bd589eef09ccdf3329a0addea0" title="Return number of scalars in the RV.">count</a>() ),<span class="stringliteral">"wrong rvn"</span> ); 
    573 <a name="l00794"></a>00794         <span class="comment">//Permutation vector of the new R</span> 
    574 <a name="l00795"></a>00795         ivec irvn = rvn.<a class="code" href="classRV.html#bb724fa4e2d9ed7bfd0993b5975018a4">dataind</a> ( <a class="code" href="classepdf.html#74da992e3f5d598da8850b646b79b9d9" title="Identified of the random variable.">rv</a> ); 
    575 <a name="l00796"></a>00796         ivec irvc = rvc.<a class="code" href="classRV.html#bb724fa4e2d9ed7bfd0993b5975018a4">dataind</a> ( <a class="code" href="classepdf.html#74da992e3f5d598da8850b646b79b9d9" title="Identified of the random variable.">rv</a> ); 
    576 <a name="l00797"></a>00797         ivec perm=<a class="code" href="group__core.html#g33c114e83980d883c5b211c47d5322a4" title="Concat two random variables.">concat</a> ( irvn , irvc ); 
    577 <a name="l00798"></a>00798         sq_T Rn ( <a class="code" href="classenorm.html#4ccc8d8514d644ef1c98d8ab023748a1" title="Covariance matrix in decomposed form.">R</a>,perm ); 
    578 <a name="l00799"></a>00799  
    579 <a name="l00800"></a>00800         <span class="comment">//fixme - could this be done in general for all sq_T?</span> 
    580 <a name="l00801"></a>00801         mat S=Rn.to_mat(); 
    581 <a name="l00802"></a>00802         <span class="comment">//fixme</span> 
    582 <a name="l00803"></a>00803         <span class="keywordtype">int</span> n=rvn.<a class="code" href="classRV.html#f5c7b8bd589eef09ccdf3329a0addea0" title="Return number of scalars in the RV.">count</a>()-1; 
    583 <a name="l00804"></a>00804         <span class="keywordtype">int</span> end=<a class="code" href="classenorm.html#4ccc8d8514d644ef1c98d8ab023748a1" title="Covariance matrix in decomposed form.">R</a>.rows()-1; 
    584 <a name="l00805"></a>00805         mat S11 = S.get ( 0,n, 0, n ); 
    585 <a name="l00806"></a>00806         mat S12 = S.get ( 0, n , rvn.<a class="code" href="classRV.html#f5c7b8bd589eef09ccdf3329a0addea0" title="Return number of scalars in the RV.">count</a>(), end ); 
    586 <a name="l00807"></a>00807         mat S22 = S.get ( rvn.<a class="code" href="classRV.html#f5c7b8bd589eef09ccdf3329a0addea0" title="Return number of scalars in the RV.">count</a>(), end, rvn.<a class="code" href="classRV.html#f5c7b8bd589eef09ccdf3329a0addea0" title="Return number of scalars in the RV.">count</a>(), end ); 
    587 <a name="l00808"></a>00808  
    588 <a name="l00809"></a>00809         vec mu1 = <a class="code" href="classenorm.html#71fde0d54bba147e00f612577f95ad20" title="mean value">mu</a> ( irvn ); 
    589 <a name="l00810"></a>00810         vec mu2 = <a class="code" href="classenorm.html#71fde0d54bba147e00f612577f95ad20" title="mean value">mu</a> ( irvc ); 
    590 <a name="l00811"></a>00811         mat A=S12*inv ( S22 ); 
    591 <a name="l00812"></a>00812         sq_T R_n ( S11 - A *S12.T() ); 
    592 <a name="l00813"></a>00813  
    593 <a name="l00814"></a>00814         <a class="code" href="classmlnorm.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="classmlnorm.html" title="Normal distributed linear function with linear function of mean value;.">mlnorm&lt;sq_T&gt;</a> ( rvn,rvc ); 
     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() ); 
    594594<a name="l00815"></a>00815  
    595 <a name="l00816"></a>00816         tmp-&gt;set_parameters ( A,mu1-A*mu2,R_n ); 
    596 <a name="l00817"></a>00817         <span class="keywordflow">return</span> tmp; 
    597 <a name="l00818"></a>00818 } 
    598 <a name="l00819"></a>00819  
     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 } 
    599600<a name="l00821"></a>00821  
    600 <a name="l00822"></a>00822 <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt; 
    601 <a name="l00823"></a>00823 std::ostream &amp;operator&lt;&lt; ( std::ostream &amp;os,  mlnorm&lt;sq_T&gt; &amp;ml ) { 
    602 <a name="l00824"></a>00824         os &lt;&lt; <span class="stringliteral">"A:"</span>&lt;&lt; ml.A&lt;&lt;endl; 
    603 <a name="l00825"></a>00825         os &lt;&lt; <span class="stringliteral">"mu:"</span>&lt;&lt; ml.mu_const&lt;&lt;endl; 
    604 <a name="l00826"></a>00826         os &lt;&lt; <span class="stringliteral">"R:"</span> &lt;&lt; ml.epdf._R().to_mat() &lt;&lt;endl; 
    605 <a name="l00827"></a>00827         <span class="keywordflow">return</span> os; 
    606 <a name="l00828"></a>00828 }; 
    607 <a name="l00829"></a>00829  
    608 <a name="l00830"></a>00830 <span class="preprocessor">#endif //EF_H</span> 
     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> 
    609612</pre></div></div> 
    610 <hr size="1"><address style="text-align: right;"><small>Generated on Thu Jan 15 10:50:25 2009 for mixpp by&nbsp; 
     613<hr size="1"><address style="text-align: right;"><small>Generated on Tue Jan 27 16:29:53 2009 for mixpp by&nbsp; 
    611614<a href="http://www.doxygen.org/index.html"> 
    612615<img src="doxygen.png" alt="doxygen" align="middle" border="0"></a> 1.5.6 </small></address>