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65	<h1>libEF.h</h1><a href="libEF_8h.html">Go to the documentation of this file.</a><div class="fragment"><pre class="fragment"><a name="l00001"></a>00001
66	<a name="l00013"></a>00013 <span class="preprocessor">#ifndef EF_H</span>
67	<a name="l00014"></a>00014 <span class="preprocessor"></span><span class="preprocessor">#define EF_H</span>
68	<a name="l00015"></a>00015 <span class="preprocessor"></span>
69	<a name="l00016"></a>00016
70	<a name="l00017"></a>00017 <span class="preprocessor">#include "<a class="code" href="libBM_8h.html" title="Bayesian Models (bm) that use Bayes rule to learn from observations.">libBM.h</a>"</span>
71	<a name="l00018"></a>00018 <span class="preprocessor">#include "../math/chmat.h"</span>
72	<a name="l00019"></a>00019 <span class="comment">//#include "../user_info.h"</span>
73	<a name="l00020"></a>00020
74	<a name="l00021"></a>00021 <span class="keyword">namespace </span>bdm
75	<a name="l00022"></a>00022 {
76	<a name="l00023"></a>00023
77	<a name="l00024"></a>00024
78	<a name="l00026"></a>00026 <span class="keyword">extern</span> Uniform_RNG UniRNG;
79	<a name="l00028"></a>00028 <span class="keyword">extern</span> Normal_RNG NorRNG;
80	<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;
81	<a name="l00031"></a>00031
82	<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>
83	<a name="l00039"></a>00039 {
84	<a name="l00040"></a>00040 <span class="keyword">public</span>:
85	<a name="l00041"></a>00041 <span class="comment">// eEF() :epdf() {};</span>
86	<a name="l00043"></a><a class="code" href="classbdm_1_1eEF.html#d5459d472d0feca7cf1fb5f65c4b9ef4">00043</a> <span class="comment"></span> <a class="code" href="classbdm_1_1eEF.html#d5459d472d0feca7cf1fb5f65c4b9ef4" title="default constructor">eEF</a> ( ) :<a class="code" href="classbdm_1_1epdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a> ( ) {};
87	<a name="l00045"></a>00045 <span class="keyword">virtual</span> <span class="keywordtype">double</span> <a class="code" href="classbdm_1_1eEF.html#cd678fc9b02007a4b8d6692e746f1bf8" title="logarithm of the normalizing constant, ">lognc</a>() <span class="keyword">const</span> =0;
88	<a name="l00047"></a><a class="code" href="classbdm_1_1eEF.html#deef7d6273ba4d5a5cf0bbd91ec7277a">00047</a> <span class="keyword">virtual</span> <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1eEF.html#deef7d6273ba4d5a5cf0bbd91ec7277a" title="TODO decide if it is really needed.">dupdate</a> ( mat &v ) {it_error ( <span class="stringliteral">"Not implemented"</span> );};
89	<a name="l00049"></a><a class="code" href="classbdm_1_1eEF.html#a4135778ecd9ab774762936c82a097c6">00049</a> <span class="keyword">virtual</span> <span class="keywordtype">double</span> <a class="code" href="classbdm_1_1eEF.html#a4135778ecd9ab774762936c82a097c6" title="Evaluate normalized log-probability.">evallog_nn</a> ( <span class="keyword">const</span> vec &val )<span class="keyword"> const</span>{it_error ( <span class="stringliteral">"Not implemented"</span> );<span class="keywordflow">return</span> 0.0;};
90	<a name="l00051"></a><a class="code" href="classbdm_1_1eEF.html#a36d06ecdd6f4c79dc122510eaccc692">00051</a> <span class="keyword">virtual</span> <span class="keywordtype">double</span> <a class="code" href="classbdm_1_1eEF.html#a36d06ecdd6f4c79dc122510eaccc692" title="Evaluate normalized log-probability.">evallog</a> ( <span class="keyword">const</span> vec &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;}
91	<a name="l00053"></a><a class="code" href="classbdm_1_1eEF.html#79a7c8ea8c02e45d410bd1d7ffd72b41">00053</a> <span class="keyword">virtual</span> vec <a class="code" href="classbdm_1_1eEF.html#a36d06ecdd6f4c79dc122510eaccc692" title="Evaluate normalized log-probability.">evallog</a> ( <span class="keyword">const</span> mat &Val )<span class="keyword"> const</span>
92	<a name="l00054"></a>00054 <span class="keyword"> </span>{
93	<a name="l00055"></a>00055 vec x ( Val.cols() );
94	<a name="l00056"></a>00056 <span class="keywordflow">for</span> ( <span class="keywordtype">int</span> i=0;i<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 ) ) ;}
95	<a name="l00057"></a>00057 <span class="keywordflow">return</span> x-<a class="code" href="classbdm_1_1eEF.html#cd678fc9b02007a4b8d6692e746f1bf8" title="logarithm of the normalizing constant, ">lognc</a>();
96	<a name="l00058"></a>00058 }
97	<a name="l00060"></a><a class="code" href="classbdm_1_1eEF.html#cf38af29e8e3d650c640509a52396053">00060</a> <span class="keyword">virtual</span> <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1eEF.html#cf38af29e8e3d650c640509a52396053" title="Power of the density, used e.g. to flatten the density.">pow</a> ( <span class="keywordtype">double</span> p ) {it_error ( <span class="stringliteral">"Not implemented"</span> );};
98	<a name="l00061"></a>00061 };
99	<a name="l00062"></a>00062
100	<a name="l00069"></a><a class="code" href="classbdm_1_1mEF.html">00069</a> <span class="keyword">class </span><a class="code" href="classbdm_1_1mEF.html" title="Exponential family model.">mEF</a> : <span class="keyword">public</span> <a class="code" href="classbdm_1_1mpdf.html" title="Conditional probability density, e.g. modeling some dependencies.">mpdf</a>
101	<a name="l00070"></a>00070 {
102	<a name="l00071"></a>00071
103	<a name="l00072"></a>00072 <span class="keyword">public</span>:
104	<a name="l00074"></a><a class="code" href="classbdm_1_1mEF.html#dd7caad7026b778af66e34480eec6f9e">00074</a> <a class="code" href="classbdm_1_1mEF.html#dd7caad7026b778af66e34480eec6f9e" title="Default constructor.">mEF</a> ( ) :<a class="code" href="classbdm_1_1mpdf.html" title="Conditional probability density, e.g. modeling some dependencies.">mpdf</a> ( ) {};
105	<a name="l00075"></a>00075 };
106	<a name="l00076"></a>00076
107	<a name="l00078"></a><a class="code" href="classbdm_1_1BMEF.html">00078</a> <span class="keyword">class </span><a class="code" href="classbdm_1_1BMEF.html" title="Estimator for Exponential family.">BMEF</a> : <span class="keyword">public</span> <a class="code" href="classbdm_1_1BM.html" title="Bayesian Model of a system, i.e. all uncertainty is modeled by probabilities.">BM</a>
108	<a name="l00079"></a>00079 {
109	<a name="l00080"></a>00080 <span class="keyword">protected</span>:
110	<a name="l00082"></a><a class="code" href="classbdm_1_1BMEF.html#1331865e10fb1ccef65bb4c47fa3be64">00082</a> <span class="keywordtype">double</span> <a class="code" href="classbdm_1_1BMEF.html#1331865e10fb1ccef65bb4c47fa3be64" title="forgetting factor">frg</a>;
111	<a name="l00084"></a><a class="code" href="classbdm_1_1BMEF.html#06e7b3ac03e10017d4288c76888e2865">00084</a> <span class="keywordtype">double</span> <a class="code" href="classbdm_1_1BMEF.html#06e7b3ac03e10017d4288c76888e2865" title="cached value of lognc() in the previous step (used in evaluation of ll )">last_lognc</a>;
112	<a name="l00085"></a>00085 <span class="keyword">public</span>:
113	<a name="l00087"></a><a class="code" href="classbdm_1_1BMEF.html#2def512872ed8a4fc3b702371ec0be55">00087</a> <a class="code" href="classbdm_1_1BMEF.html#2def512872ed8a4fc3b702371ec0be55" title="Default constructor (=empty constructor).">BMEF</a> ( <span class="keywordtype">double</span> frg0=1.0 ) :<a class="code" href="classbdm_1_1BM.html" title="Bayesian Model of a system, i.e. all uncertainty is modeled by probabilities.">BM</a> ( ), <a class="code" href="classbdm_1_1BMEF.html#1331865e10fb1ccef65bb4c47fa3be64" title="forgetting factor">frg</a> ( frg0 ) {}
114	<a name="l00089"></a><a class="code" href="classbdm_1_1BMEF.html#9662379513101405e159e76717104e62">00089</a> <a class="code" href="classbdm_1_1BMEF.html#9662379513101405e159e76717104e62" title="Copy constructor.">BMEF</a> ( <span class="keyword">const</span> <a class="code" href="classbdm_1_1BMEF.html" title="Estimator for Exponential family.">BMEF</a> &B ) :<a class="code" href="classbdm_1_1BM.html" title="Bayesian Model of a system, i.e. all uncertainty is modeled by probabilities.">BM</a> ( B ), <a class="code" href="classbdm_1_1BMEF.html#1331865e10fb1ccef65bb4c47fa3be64" title="forgetting factor">frg</a> ( B.<a class="code" href="classbdm_1_1BMEF.html#1331865e10fb1ccef65bb4c47fa3be64" title="forgetting factor">frg</a> ), <a class="code" href="classbdm_1_1BMEF.html#06e7b3ac03e10017d4288c76888e2865" title="cached value of lognc() in the previous step (used in evaluation of ll )">last_lognc</a> ( B.<a class="code" href="classbdm_1_1BMEF.html#06e7b3ac03e10017d4288c76888e2865" title="cached value of lognc() in the previous step (used in evaluation of ll )">last_lognc</a> ) {}
115	<a name="l00091"></a><a class="code" href="classbdm_1_1BMEF.html#d2b528b7a41ca67163152142f5404051">00091</a> <span class="keyword">virtual</span> <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1BMEF.html#d2b528b7a41ca67163152142f5404051" title="get statistics from another model">set_statistics</a> ( <span class="keyword">const</span> <a class="code" href="classbdm_1_1BMEF.html" title="Estimator for Exponential family.">BMEF</a>* BM0 ) {it_error ( <span class="stringliteral">"Not implemented"</span> );};
116	<a name="l00093"></a><a class="code" href="classbdm_1_1BMEF.html#bf58deb99af2a6cc674f13ff90300de6">00093</a> <span class="keyword">virtual</span> <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1BMEF.html#bf58deb99af2a6cc674f13ff90300de6" title="Weighted update of sufficient statistics (Bayes rule).">bayes</a> ( <span class="keyword">const</span> vec &data, <span class="keyword">const</span> <span class="keywordtype">double</span> w ) {};
117	<a name="l00094"></a>00094 <span class="comment">//original Bayes</span>
118	<a name="l00095"></a>00095 <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1BMEF.html#bf58deb99af2a6cc674f13ff90300de6" title="Weighted update of sufficient statistics (Bayes rule).">bayes</a> ( <span class="keyword">const</span> vec &dt );
119	<a name="l00097"></a><a class="code" href="classbdm_1_1BMEF.html#b2916a2e71a958665054473124d5e749">00097</a> <span class="keyword">virtual</span> <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1BMEF.html#b2916a2e71a958665054473124d5e749" title="Flatten the posterior according to the given BMEF (of the same type!).">flatten</a> ( <span class="keyword">const</span> <a class="code" href="classbdm_1_1BMEF.html" title="Estimator for Exponential family.">BMEF</a> * B ) {it_error ( <span class="stringliteral">"Not implemented"</span> );}
120	<a name="l00099"></a>00099 <span class="comment">// virtual void flatten ( double nu0 ) {it_error ( "Not implemented" );}</span>
121	<a name="l00100"></a>00100
122	<a name="l00101"></a><a class="code" href="classbdm_1_1BMEF.html#62d2e4691bed41a1efa6b9c2e35e5c67">00101</a> <a class="code" href="classbdm_1_1BMEF.html" title="Estimator for Exponential family.">BMEF</a>* <a class="code" href="classbdm_1_1BMEF.html#62d2e4691bed41a1efa6b9c2e35e5c67" title="Flatten the posterior as if to keep nu0 data.">_copy_</a> ()<span class="keyword"> const </span>{it_error ( <span class="stringliteral">"function _copy_ not implemented for this BM"</span> ); <span class="keywordflow">return</span> NULL;};
123	<a name="l00102"></a>00102 };
124	<a name="l00103"></a>00103
125	<a name="l00104"></a>00104 <span class="keyword">template</span><<span class="keyword">class</span> sq_T>
126	<a name="l00105"></a>00105 <span class="keyword">class </span>mlnorm;
127	<a name="l00106"></a>00106
128	<a name="l00112"></a>00112 <span class="keyword">template</span><<span class="keyword">class</span> sq_T>
129	<a name="l00113"></a><a class="code" href="classbdm_1_1enorm.html">00113</a> <span class="keyword">class </span><a class="code" href="classbdm_1_1enorm.html" title="Gaussian density with positive definite (decomposed) covariance matrix.">enorm</a> : <span class="keyword">public</span> <a class="code" href="classbdm_1_1eEF.html" title="General conjugate exponential family posterior density.">eEF</a>
130	<a name="l00114"></a>00114 {
131	<a name="l00115"></a>00115 <span class="keyword">protected</span>:
132	<a name="l00117"></a><a class="code" href="classbdm_1_1enorm.html#c702a194720853570d08b65482f842c7">00117</a> vec <a class="code" href="classbdm_1_1enorm.html#c702a194720853570d08b65482f842c7" title="mean value">mu</a>;
133	<a name="l00119"></a><a class="code" href="classbdm_1_1enorm.html#2d92dde696b2a7a5b10ddef5d22ba2c2">00119</a> sq_T <a class="code" href="classbdm_1_1enorm.html#2d92dde696b2a7a5b10ddef5d22ba2c2" title="Covariance matrix in decomposed form.">R</a>;
134	<a name="l00120"></a>00120 <span class="keyword">public</span>:
135	<a name="l00123"></a>00123
136	<a name="l00124"></a>00124 <a class="code" href="classbdm_1_1enorm.html" title="Gaussian density with positive definite (decomposed) covariance matrix.">enorm</a> ( ) :<a class="code" href="classbdm_1_1eEF.html" title="General conjugate exponential family posterior density.">eEF</a> ( ), <a class="code" href="classbdm_1_1enorm.html#c702a194720853570d08b65482f842c7" title="mean value">mu</a> ( ),<a class="code" href="classbdm_1_1enorm.html#2d92dde696b2a7a5b10ddef5d22ba2c2" title="Covariance matrix in decomposed form.">R</a> ( ) {};
137	<a name="l00125"></a>00125 <a class="code" href="classbdm_1_1enorm.html" title="Gaussian density with positive definite (decomposed) covariance matrix.">enorm</a> ( <span class="keyword">const</span> vec &<a class="code" href="classbdm_1_1enorm.html#c702a194720853570d08b65482f842c7" title="mean value">mu</a>,<span class="keyword">const</span> sq_T &<a class="code" href="classbdm_1_1enorm.html#2d92dde696b2a7a5b10ddef5d22ba2c2" title="Covariance matrix in decomposed form.">R</a> ) {set_parameters ( mu,R );}
138	<a name="l00126"></a>00126 <span class="keywordtype">void</span> set_parameters ( <span class="keyword">const</span> vec &<a class="code" href="classbdm_1_1enorm.html#c702a194720853570d08b65482f842c7" title="mean value">mu</a>,<span class="keyword">const</span> sq_T &<a class="code" href="classbdm_1_1enorm.html#2d92dde696b2a7a5b10ddef5d22ba2c2" title="Covariance matrix in decomposed form.">R</a> );
139	<a name="l00128"></a>00128
140	<a name="l00131"></a>00131
141	<a name="l00133"></a>00133 <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1enorm.html#d2e0d3a1e30ab3ab04df2d0c43ae74a2" title="dupdate in exponential form (not really handy)">dupdate</a> ( mat &v,<span class="keywordtype">double</span> nu=1.0 );
142	<a name="l00134"></a>00134
143	<a name="l00135"></a>00135 vec <a class="code" href="classbdm_1_1enorm.html#e1a48f52351ec3a349bd443b713b1766" title="Returns a sample, from density .">sample</a>() <span class="keyword">const</span>;
144	<a name="l00136"></a>00136 mat <a class="code" href="classbdm_1_1enorm.html#e1a48f52351ec3a349bd443b713b1766" title="Returns a sample, from density .">sample</a> ( <span class="keywordtype">int</span> N ) <span class="keyword">const</span>;
145	<a name="l00137"></a>00137 <span class="keywordtype">double</span> <a class="code" href="classbdm_1_1enorm.html#e13aeed5b543b2179bacdc4fa2ae47a3" title="Evaluate normalized log-probability.">evallog_nn</a> ( <span class="keyword">const</span> vec &val ) <span class="keyword">const</span>;
146	<a name="l00138"></a>00138 <span class="keywordtype">double</span> <a class="code" href="classbdm_1_1enorm.html#25785343aff102cc5df1cab08ba16d32" title="logarithm of the normalizing constant, ">lognc</a> () <span class="keyword">const</span>;
147	<a name="l00139"></a><a class="code" href="classbdm_1_1enorm.html#b2fa2915c35366392fe9bb022ca1a600">00139</a> vec <a class="code" href="classbdm_1_1enorm.html#b2fa2915c35366392fe9bb022ca1a600" title="return expected value">mean</a>()<span class="keyword"> const </span>{<span class="keywordflow">return</span> <a class="code" href="classbdm_1_1enorm.html#c702a194720853570d08b65482f842c7" title="mean value">mu</a>;}
148	<a name="l00140"></a><a class="code" href="classbdm_1_1enorm.html#729c75ef0fa8abae03d58ad1f81e6773">00140</a> vec <a class="code" href="classbdm_1_1enorm.html#729c75ef0fa8abae03d58ad1f81e6773" title="return expected variance (not covariance!)">variance</a>()<span class="keyword"> const </span>{<span class="keywordflow">return</span> diag ( <a class="code" href="classbdm_1_1enorm.html#2d92dde696b2a7a5b10ddef5d22ba2c2" title="Covariance matrix in decomposed form.">R</a>.to_mat() );}
149	<a name="l00141"></a>00141 <span class="comment">// mlnorm<sq_T>* condition ( const RV &rvn ) const ; <=========== fails to cmpile. Why?</span>
150	<a name="l00142"></a>00142 <a class="code" href="classbdm_1_1mpdf.html" title="Conditional probability density, e.g. modeling some dependencies.">mpdf</a>* <a class="code" href="classbdm_1_1enorm.html#baea4d49c657342b58297d68cda16d26" title="Return conditional density on the given RV, the remaining rvs will be in conditioning...">condition</a> ( <span class="keyword">const</span> <a class="code" href="classbdm_1_1RV.html" title="Class representing variables, most often random variables.">RV</a> &rvn ) <span class="keyword">const</span> ;
151	<a name="l00143"></a>00143 <a class="code" href="classbdm_1_1enorm.html" title="Gaussian density with positive definite (decomposed) covariance matrix.">enorm<sq_T></a>* <a class="code" href="classbdm_1_1enorm.html#c2996bdaffad38fbe0fc776db54c9d80" 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> &<a class="code" href="classbdm_1_1epdf.html#62c5b8ff71d9ebe6cd58d3c342eb1dc8" title="Description of the random variable.">rv</a> ) <span class="keyword">const</span>;
152	<a name="l00144"></a>00144 <span class="comment">// epdf* marginal ( const RV &rv ) const;</span>
153	<a name="l00146"></a>00146 <span class="comment"></span>
154	<a name="l00149"></a>00149
155	<a name="l00150"></a>00150 vec& _mu() {<span class="keywordflow">return</span> <a class="code" href="classbdm_1_1enorm.html#c702a194720853570d08b65482f842c7" title="mean value">mu</a>;}
156	<a name="l00151"></a>00151 <span class="keywordtype">void</span> set_mu ( <span class="keyword">const</span> vec mu0 ) { <a class="code" href="classbdm_1_1enorm.html#c702a194720853570d08b65482f842c7" title="mean value">mu</a>=mu0;}
157	<a name="l00152"></a>00152 sq_T& _R() {<span class="keywordflow">return</span> <a class="code" href="classbdm_1_1enorm.html#2d92dde696b2a7a5b10ddef5d22ba2c2" title="Covariance matrix in decomposed form.">R</a>;}
158	<a name="l00153"></a>00153 <span class="keyword">const</span> sq_T& _R()<span class="keyword"> const </span>{<span class="keywordflow">return</span> <a class="code" href="classbdm_1_1enorm.html#2d92dde696b2a7a5b10ddef5d22ba2c2" title="Covariance matrix in decomposed form.">R</a>;}
159	<a name="l00155"></a>00155
160	<a name="l00156"></a>00156 };
161	<a name="l00157"></a>00157
162	<a name="l00164"></a><a class="code" href="classbdm_1_1egiw.html">00164</a> <span class="keyword">class </span><a class="code" href="classbdm_1_1egiw.html" title="Gauss-inverse-Wishart density stored in LD form.">egiw</a> : <span class="keyword">public</span> <a class="code" href="classbdm_1_1eEF.html" title="General conjugate exponential family posterior density.">eEF</a>
163	<a name="l00165"></a>00165 {
164	<a name="l00166"></a>00166 <span class="keyword">protected</span>:
165	<a name="l00168"></a><a class="code" href="classbdm_1_1egiw.html#ae56852845c6af176fd9017dbebbbd52">00168</a> <a class="code" href="classldmat.html" title="Matrix stored in LD form, (commonly known as UD).">ldmat</a> <a class="code" href="classbdm_1_1egiw.html#ae56852845c6af176fd9017dbebbbd52" title="Extended information matrix of sufficient statistics.">V</a>;
166	<a name="l00170"></a><a class="code" href="classbdm_1_1egiw.html#447eacf19d4f4083872686f044814dc4">00170</a> <span class="keywordtype">double</span> <a class="code" href="classbdm_1_1egiw.html#447eacf19d4f4083872686f044814dc4" title="Number of data records (degrees of freedom) of sufficient statistics.">nu</a>;
167	<a name="l00172"></a><a class="code" href="classbdm_1_1egiw.html#23e4d78bea7e98840f3da30e76a2b57a">00172</a> <span class="keywordtype">int</span> <a class="code" href="classbdm_1_1egiw.html#23e4d78bea7e98840f3da30e76a2b57a" title="Dimension of the output.">dimx</a>;
168	<a name="l00174"></a><a class="code" href="classbdm_1_1egiw.html#322414c32d9a21a006a5aab0311f64fd">00174</a> <span class="keywordtype">int</span> <a class="code" href="classbdm_1_1egiw.html#322414c32d9a21a006a5aab0311f64fd" title="Dimension of the regressor.">nPsi</a>;
169	<a name="l00175"></a>00175 <span class="keyword">public</span>:
170	<a name="l00178"></a>00178 <a class="code" href="classbdm_1_1egiw.html" title="Gauss-inverse-Wishart density stored in LD form.">egiw</a>() :<a class="code" href="classbdm_1_1eEF.html" title="General conjugate exponential family posterior density.">eEF</a>() {};
171	<a name="l00179"></a>00179 <a class="code" href="classbdm_1_1egiw.html" title="Gauss-inverse-Wishart density stored in LD form.">egiw</a> ( <span class="keywordtype">int</span> dimx0, <a class="code" href="classldmat.html" title="Matrix stored in LD form, (commonly known as UD).">ldmat</a> V0, <span class="keywordtype">double</span> nu0=-1.0 ) :<a class="code" href="classbdm_1_1eEF.html" title="General conjugate exponential family posterior density.">eEF</a>() {set_parameters ( dimx0,V0, nu0 );};
172	<a name="l00180"></a>00180
173	<a name="l00181"></a>00181 <span class="keywordtype">void</span> set_parameters ( <span class="keywordtype">int</span> dimx0, <a class="code" href="classldmat.html" title="Matrix stored in LD form, (commonly known as UD).">ldmat</a> V0, <span class="keywordtype">double</span> nu0=-1.0 )
174	<a name="l00182"></a>00182 {
175	<a name="l00183"></a>00183 <a class="code" href="classbdm_1_1egiw.html#23e4d78bea7e98840f3da30e76a2b57a" title="Dimension of the output.">dimx</a>=dimx0;
176	<a name="l00184"></a>00184 <a class="code" href="classbdm_1_1egiw.html#322414c32d9a21a006a5aab0311f64fd" title="Dimension of the regressor.">nPsi</a> = V0.<a class="code" href="group__math.html#g96dfb21865db4f5bd36fa70f9b0b1163" title="access function">rows</a>()-<a class="code" href="classbdm_1_1egiw.html#23e4d78bea7e98840f3da30e76a2b57a" title="Dimension of the output.">dimx</a>;
177	<a name="l00185"></a>00185 <a class="code" href="classbdm_1_1epdf.html#16adac20ec7fe07e1ea0b27d917788ce" title="dimension of the random variable">dim</a> = <a class="code" href="classbdm_1_1egiw.html#23e4d78bea7e98840f3da30e76a2b57a" title="Dimension of the output.">dimx</a>* ( <a class="code" href="classbdm_1_1egiw.html#23e4d78bea7e98840f3da30e76a2b57a" title="Dimension of the output.">dimx</a>+<a class="code" href="classbdm_1_1egiw.html#322414c32d9a21a006a5aab0311f64fd" title="Dimension of the regressor.">nPsi</a> ); <span class="comment">// size(R) + size(Theta)</span>
178	<a name="l00186"></a>00186
179	<a name="l00187"></a>00187 <a class="code" href="classbdm_1_1egiw.html#ae56852845c6af176fd9017dbebbbd52" title="Extended information matrix of sufficient statistics.">V</a>=V0;
180	<a name="l00188"></a>00188 <span class="keywordflow">if</span> ( nu0<0 )
181	<a name="l00189"></a>00189 {
182	<a name="l00190"></a>00190 <a class="code" href="classbdm_1_1egiw.html#447eacf19d4f4083872686f044814dc4" title="Number of data records (degrees of freedom) of sufficient statistics.">nu</a> = 0.1 +<a class="code" href="classbdm_1_1egiw.html#322414c32d9a21a006a5aab0311f64fd" title="Dimension of the regressor.">nPsi</a> +2*<a class="code" href="classbdm_1_1egiw.html#23e4d78bea7e98840f3da30e76a2b57a" title="Dimension of the output.">dimx</a> +2; <span class="comment">// +2 assures finite expected value of R</span>
183	<a name="l00191"></a>00191 <span class="comment">// terms before that are sufficient for finite normalization</span>
184	<a name="l00192"></a>00192 }
185	<a name="l00193"></a>00193 <span class="keywordflow">else</span>
186	<a name="l00194"></a>00194 {
187	<a name="l00195"></a>00195 <a class="code" href="classbdm_1_1egiw.html#447eacf19d4f4083872686f044814dc4" title="Number of data records (degrees of freedom) of sufficient statistics.">nu</a>=nu0;
188	<a name="l00196"></a>00196 }
189	<a name="l00197"></a>00197 }
190	<a name="l00199"></a>00199
191	<a name="l00200"></a>00200 vec <a class="code" href="classbdm_1_1egiw.html#920f21548b7a3723923dd108fe514c61" title="Returns a sample, from density .">sample</a>() <span class="keyword">const</span>;
192	<a name="l00201"></a>00201 vec <a class="code" href="classbdm_1_1egiw.html#df70c05f918c3a6f86d60f10c1fd6ba2" title="return expected value">mean</a>() <span class="keyword">const</span>;
193	<a name="l00202"></a>00202 vec <a class="code" href="classbdm_1_1egiw.html#c1ecc406613cc2341225dc10c3d3b46a" title="return expected variance (not covariance!)">variance</a>() <span class="keyword">const</span>;
194	<a name="l00203"></a>00203
195	<a name="l00205"></a>00205 vec <a class="code" href="classbdm_1_1egiw.html#66d2ba9295c306012b309efcc9e516f0" title="LS estimate of .">est_theta</a>() <span class="keyword">const</span>;
196	<a name="l00206"></a>00206
197	<a name="l00208"></a>00208 <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#88c321a2051d1afdbb31a098896a717b" title="Covariance of the LS estimate.">est_theta_cov</a>() <span class="keyword">const</span>;
198	<a name="l00209"></a>00209
199	<a name="l00210"></a>00210 <span class="keywordtype">void</span> mean_mat ( mat &M, mat&R ) <span class="keyword">const</span>;
200	<a name="l00212"></a>00212 <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 &val ) <span class="keyword">const</span>;
201	<a name="l00213"></a>00213 <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>;
202	<a name="l00214"></a><a class="code" href="classbdm_1_1egiw.html#8e610e95401a11baf34f65e16ecd87be">00214</a> <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1egiw.html#8e610e95401a11baf34f65e16ecd87be" title="Power of the density, used e.g. to flatten the density.">pow</a> ( <span class="keywordtype">double</span> p ) {V=p;<a class="code" href="classbdm_1_1egiw.html#447eacf19d4f4083872686f044814dc4" title="Number of data records (degrees of freedom) of sufficient statistics.">nu</a>=p;};
203	<a name="l00215"></a>00215
204	<a name="l00218"></a>00218
205	<a name="l00219"></a>00219 <a class="code" href="classldmat.html" title="Matrix stored in LD form, (commonly known as UD).">ldmat</a>& _V() {<span class="keywordflow">return</span> V;}
206	<a name="l00220"></a>00220 <span class="keyword">const</span> <a class="code" href="classldmat.html" title="Matrix stored in LD form, (commonly known as UD).">ldmat</a>& _V()<span class="keyword"> const </span>{<span class="keywordflow">return</span> V;}
207	<a name="l00221"></a>00221 <span class="keywordtype">double</span>& _nu() {<span class="keywordflow">return</span> <a class="code" href="classbdm_1_1egiw.html#447eacf19d4f4083872686f044814dc4" title="Number of data records (degrees of freedom) of sufficient statistics.">nu</a>;}
208	<a name="l00222"></a>00222 <span class="keyword">const</span> <span class="keywordtype">double</span>& _nu()<span class="keyword"> const </span>{<span class="keywordflow">return</span> <a class="code" href="classbdm_1_1egiw.html#447eacf19d4f4083872686f044814dc4" title="Number of data records (degrees of freedom) of sufficient statistics.">nu</a>;}
209	<a name="l00224"></a>00224 };
210	<a name="l00225"></a>00225
211	<a name="l00234"></a><a class="code" href="classbdm_1_1eDirich.html">00234</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>
212	<a name="l00235"></a>00235 {
213	<a name="l00236"></a>00236 <span class="keyword">protected</span>:
214	<a name="l00238"></a><a class="code" href="classbdm_1_1eDirich.html#f25886a49b4667af61245de81c83b5d2">00238</a> vec <a class="code" href="classbdm_1_1eDirich.html#f25886a49b4667af61245de81c83b5d2" title="sufficient statistics">beta</a>;
215	<a name="l00239"></a>00239 <span class="keyword">public</span>:
216	<a name="l00242"></a>00242
217	<a name="l00243"></a>00243 <a class="code" href="classbdm_1_1eDirich.html" title="Dirichlet posterior density.">eDirich</a> () : <a class="code" href="classbdm_1_1eEF.html" title="General conjugate exponential family posterior density.">eEF</a> ( ) {};
218	<a name="l00244"></a>00244 <a class="code" href="classbdm_1_1eDirich.html" title="Dirichlet posterior density.">eDirich</a> ( <span class="keyword">const</span> <a class="code" href="classbdm_1_1eDirich.html" title="Dirichlet posterior density.">eDirich</a> &D0 ) : <a class="code" href="classbdm_1_1eEF.html" title="General conjugate exponential family posterior density.">eEF</a> () {set_parameters ( D0.<a class="code" href="classbdm_1_1eDirich.html#f25886a49b4667af61245de81c83b5d2" title="sufficient statistics">beta</a> );};
219	<a name="l00245"></a>00245 eDirich ( <span class="keyword">const</span> vec &beta0 ) {set_parameters ( beta0 );};
220	<a name="l00246"></a>00246 <span class="keywordtype">void</span> set_parameters ( <span class="keyword">const</span> vec &beta0 )
221	<a name="l00247"></a>00247 {
222	<a name="l00248"></a>00248 <a class="code" href="classbdm_1_1eDirich.html#f25886a49b4667af61245de81c83b5d2" title="sufficient statistics">beta</a>= beta0;
223	<a name="l00249"></a>00249 <a class="code" href="classbdm_1_1epdf.html#16adac20ec7fe07e1ea0b27d917788ce" title="dimension of the random variable">dim</a> = <a class="code" href="classbdm_1_1eDirich.html#f25886a49b4667af61245de81c83b5d2" title="sufficient statistics">beta</a>.length();
224	<a name="l00250"></a>00250 }
225	<a name="l00252"></a>00252
226	<a name="l00253"></a><a class="code" href="classbdm_1_1eDirich.html#3290613d31d58daa8a45a54b003871fc">00253</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 );};
227	<a name="l00254"></a><a class="code" href="classbdm_1_1eDirich.html#cb343355ec791298bb5a3404cd482fb6">00254</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>/sum(<a class="code" href="classbdm_1_1eDirich.html#f25886a49b4667af61245de81c83b5d2" title="sufficient statistics">beta</a>);};
228	<a name="l00255"></a><a class="code" href="classbdm_1_1eDirich.html#43c547a2507e233706f92712d8c2aacc">00255</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="keywordtype">double</span> gamma =sum(<a class="code" href="classbdm_1_1eDirich.html#f25886a49b4667af61245de81c83b5d2" title="sufficient statistics">beta</a>); <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 ) ) / ( gamma* ( gamma+1 ) );}
229	<a name="l00257"></a><a class="code" href="classbdm_1_1eDirich.html#e09a24938e80c3d94b0ee842d1552318">00257</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 &val )<span class="keyword"> const</span>
230	<a name="l00258"></a>00258 <span class="keyword"> </span>{
231	<a name="l00259"></a>00259 <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> );
232	<a name="l00260"></a>00260 <span class="keywordflow">return</span> tmp;
233	<a name="l00261"></a>00261 };
234	<a name="l00262"></a><a class="code" href="classbdm_1_1eDirich.html#279a99f6266c82fe2273e83841f19eb2">00262</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>
235	<a name="l00263"></a>00263 <span class="keyword"> </span>{
236	<a name="l00264"></a>00264 <span class="keywordtype">double</span> tmp;
237	<a name="l00265"></a>00265 <span class="keywordtype">double</span> gam=sum ( <a class="code" href="classbdm_1_1eDirich.html#f25886a49b4667af61245de81c83b5d2" title="sufficient statistics">beta</a> );
238	<a name="l00266"></a>00266 <span class="keywordtype">double</span> lgb=0.0;
239	<a name="l00267"></a>00267 <span class="keywordflow">for</span> ( <span class="keywordtype">int</span> i=0;i<<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 ) );}
240	<a name="l00268"></a>00268 tmp= lgb-lgamma ( gam );
241	<a name="l00269"></a>00269 it_assert_debug ( std::isfinite ( tmp ),<span class="stringliteral">"Infinite value"</span> );
242	<a name="l00270"></a>00270 <span class="keywordflow">return</span> tmp;
243	<a name="l00271"></a>00271 };
244	<a name="l00273"></a><a class="code" href="classbdm_1_1eDirich.html#175e0add26d2105c28d8121eefb9e324">00273</a> vec& <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>;}
245	<a name="l00275"></a>00275 };
246	<a name="l00276"></a>00276
247	<a name="l00278"></a><a class="code" href="classbdm_1_1multiBM.html">00278</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>
248	<a name="l00279"></a>00279 {
249	<a name="l00280"></a>00280 <span class="keyword">protected</span>:
250	<a name="l00282"></a><a class="code" href="classbdm_1_1multiBM.html#9ecc6878abbd20eb8d8e43b6ab3f941a">00282</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>;
251	<a name="l00284"></a><a class="code" href="classbdm_1_1multiBM.html#044263356944c92209eecd39a5187d25">00284</a> vec &<a class="code" href="classbdm_1_1multiBM.html#044263356944c92209eecd39a5187d25" title="Pointer inside est to sufficient statistics.">beta</a>;
252	<a name="l00285"></a>00285 <span class="keyword">public</span>:
253	<a name="l00287"></a><a class="code" href="classbdm_1_1multiBM.html#c4dd6d9522a8a605776d21bac9bd9daf">00287</a> <a class="code" href="classbdm_1_1multiBM.html#c4dd6d9522a8a605776d21bac9bd9daf" title="Default constructor.">multiBM</a> ( ) : <a class="code" href="classbdm_1_1BMEF.html" title="Estimator for Exponential family.">BMEF</a> ( ),<a class="code" href="classbdm_1_1multiBM.html#9ecc6878abbd20eb8d8e43b6ab3f941a" title="Conjugate prior and posterior.">est</a> ( ),<a class="code" href="classbdm_1_1multiBM.html#044263356944c92209eecd39a5187d25" title="Pointer inside est to sufficient statistics.">beta</a> ( <a class="code" href="classbdm_1_1multiBM.html#9ecc6878abbd20eb8d8e43b6ab3f941a" title="Conjugate prior and posterior.">est</a>._beta() )
254	<a name="l00288"></a>00288 {
255	<a name="l00289"></a>00289 <span class="keywordflow">if</span> ( <a class="code" href="classbdm_1_1multiBM.html#044263356944c92209eecd39a5187d25" title="Pointer inside est to sufficient statistics.">beta</a>.length() >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>();}
256	<a name="l00290"></a>00290 <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;}
257	<a name="l00291"></a>00291 }
258	<a name="l00293"></a><a class="code" href="classbdm_1_1multiBM.html#c4378cf8037f6bed29c74eea63344b31">00293</a> <a class="code" href="classbdm_1_1multiBM.html#c4378cf8037f6bed29c74eea63344b31" title="Copy constructor.">multiBM</a> ( <span class="keyword">const</span> <a class="code" href="classbdm_1_1multiBM.html" title="Estimator for Multinomial density.">multiBM</a> &B ) : <a class="code" href="classbdm_1_1BMEF.html" title="Estimator for Exponential family.">BMEF</a> ( B ),<a class="code" href="classbdm_1_1multiBM.html#9ecc6878abbd20eb8d8e43b6ab3f941a" title="Conjugate prior and posterior.">est</a> ( B.<a class="code" href="classbdm_1_1multiBM.html#9ecc6878abbd20eb8d8e43b6ab3f941a" title="Conjugate prior and posterior.">est</a> ),<a class="code" href="classbdm_1_1multiBM.html#044263356944c92209eecd39a5187d25" title="Pointer inside est to sufficient statistics.">beta</a> ( <a class="code" href="classbdm_1_1multiBM.html#9ecc6878abbd20eb8d8e43b6ab3f941a" title="Conjugate prior and posterior.">est</a>._beta() ) {}
259	<a name="l00295"></a><a class="code" href="classbdm_1_1multiBM.html#dbe6b90d410dc062a233d1dc09eeba52">00295</a> <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1multiBM.html#dbe6b90d410dc062a233d1dc09eeba52" title="Sets sufficient statistics to match that of givefrom mB0.">set_statistics</a> ( <span class="keyword">const</span> <a class="code" href="classbdm_1_1BM.html" title="Bayesian Model of a system, i.e. all uncertainty is modeled by probabilities.">BM</a>* mB0 ) {<span class="keyword">const</span> <a class="code" href="classbdm_1_1multiBM.html" title="Estimator for Multinomial density.">multiBM</a>* mB=<span class="keyword">dynamic_cast<</span><span class="keyword">const </span><a class="code" href="classbdm_1_1multiBM.html" title="Estimator for Multinomial density.">multiBM</a>*<span class="keyword">></span> ( mB0 ); <a class="code" href="classbdm_1_1multiBM.html#044263356944c92209eecd39a5187d25" title="Pointer inside est to sufficient statistics.">beta</a>=mB-><a class="code" href="classbdm_1_1multiBM.html#044263356944c92209eecd39a5187d25" title="Pointer inside est to sufficient statistics.">beta</a>;}
260	<a name="l00296"></a><a class="code" href="classbdm_1_1multiBM.html#1e4bf41b61937fd80f34049742e23f95">00296</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 &dt )
261	<a name="l00297"></a>00297 {
262	<a name="l00298"></a>00298 <span class="keywordflow">if</span> ( <a class="code" href="classbdm_1_1BMEF.html#1331865e10fb1ccef65bb4c47fa3be64" title="forgetting factor">frg</a><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>();}
263	<a name="l00299"></a>00299 <a class="code" href="classbdm_1_1multiBM.html#044263356944c92209eecd39a5187d25" title="Pointer inside est to sufficient statistics.">beta</a>+=dt;
264	<a name="l00300"></a>00300 <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>;}
265	<a name="l00301"></a>00301 }
266	<a name="l00302"></a><a class="code" href="classbdm_1_1multiBM.html#e157b607c1e3fa91d42aeea44458e2bf">00302</a> <span class="keywordtype">double</span> <a class="code" href="classbdm_1_1multiBM.html#e157b607c1e3fa91d42aeea44458e2bf">logpred</a> ( <span class="keyword">const</span> vec &dt )<span class="keyword"> const</span>
267	<a name="l00303"></a>00303 <span class="keyword"> </span>{
268	<a name="l00304"></a>00304 <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> );
269	<a name="l00305"></a>00305 vec &<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>();
270	<a name="l00306"></a>00306
271	<a name="l00307"></a>00307 <span class="keywordtype">double</span> lll;
272	<a name="l00308"></a>00308 <span class="keywordflow">if</span> ( <a class="code" href="classbdm_1_1BMEF.html#1331865e10fb1ccef65bb4c47fa3be64" title="forgetting factor">frg</a><1.0 )
273	<a name="l00309"></a>00309 {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>();}
274	<a name="l00310"></a>00310 <span class="keywordflow">else</span>
275	<a name="l00311"></a>00311 <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>;}
276	<a name="l00312"></a>00312 <span class="keywordflow">else</span>{lll=pred.<a class="code" href="classbdm_1_1eDirich.html#279a99f6266c82fe2273e83841f19eb2" title="logarithm of the normalizing constant, ">lognc</a>();}
277	<a name="l00313"></a>00313
278	<a name="l00314"></a>00314 beta+=dt;
279	<a name="l00315"></a>00315 <span class="keywordflow">return</span> pred.<a class="code" href="classbdm_1_1eDirich.html#279a99f6266c82fe2273e83841f19eb2" title="logarithm of the normalizing constant, ">lognc</a>()-lll;
280	<a name="l00316"></a>00316 }
281	<a name="l00317"></a><a class="code" href="classbdm_1_1multiBM.html#aaeb18c989088feb8d26d300e4971732">00317</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 )
282	<a name="l00318"></a>00318 {
283	<a name="l00319"></a>00319 <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<</span><span class="keyword">const </span><a class="code" href="classbdm_1_1multiBM.html" title="Estimator for Multinomial density.">multiBM</a>*<span class="keyword">></span> ( B );
284	<a name="l00320"></a>00320 <span class="comment">// sum(beta) should be equal to sum(B.beta)</span>
285	<a name="l00321"></a>00321 <span class="keyword">const</span> vec &Eb=E-><a class="code" href="classbdm_1_1multiBM.html#044263356944c92209eecd39a5187d25" title="Pointer inside est to sufficient statistics.">beta</a>;<span class="comment">//const_cast<multiBM*> ( E )->_beta();</span>
286	<a name="l00322"></a>00322 <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> ) );
287	<a name="l00323"></a>00323 <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>();}
288	<a name="l00324"></a>00324 }
289	<a name="l00325"></a>00325 <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>& posterior()<span class="keyword"> const </span>{<span class="keywordflow">return</span> <a class="code" href="classbdm_1_1multiBM.html#9ecc6878abbd20eb8d8e43b6ab3f941a" title="Conjugate prior and posterior.">est</a>;};
290	<a name="l00326"></a>00326 <span class="keyword">const</span> eDirich* _e()<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>;};
291	<a name="l00327"></a>00327 <span class="keywordtype">void</span> set_parameters ( <span class="keyword">const</span> vec &beta0 )
292	<a name="l00328"></a>00328 {
293	<a name="l00329"></a>00329 <a class="code" href="classbdm_1_1multiBM.html#9ecc6878abbd20eb8d8e43b6ab3f941a" title="Conjugate prior and posterior.">est</a>.set_parameters ( beta0 );
294	<a name="l00330"></a>00330 <span class="keywordflow">if</span> ( <a class="code" href="classbdm_1_1BM.html#faff0ad12556fe7dc0e2807d4fd938ee" title="If true, the filter will compute likelihood of the data record and store it in ll...">evalll</a> ) {<a class="code" href="classbdm_1_1BMEF.html#06e7b3ac03e10017d4288c76888e2865" title="cached value of lognc() in the previous step (used in evaluation of ll )">last_lognc</a>=<a class="code" href="classbdm_1_1multiBM.html#9ecc6878abbd20eb8d8e43b6ab3f941a" title="Conjugate prior and posterior.">est</a>.lognc();}
295	<a name="l00331"></a>00331 }
296	<a name="l00332"></a>00332 };
297	<a name="l00333"></a>00333
298	<a name="l00343"></a><a class="code" href="classbdm_1_1egamma.html">00343</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>
299	<a name="l00344"></a>00344 {
300	<a name="l00345"></a>00345 <span class="keyword">protected</span>:
301	<a name="l00347"></a><a class="code" href="classbdm_1_1egamma.html#0901ec983e66b8337aaa506e13b122fa">00347</a> vec <a class="code" href="classbdm_1_1egamma.html#0901ec983e66b8337aaa506e13b122fa" title="Vector .">alpha</a>;
302	<a name="l00349"></a><a class="code" href="classbdm_1_1egamma.html#457bfb1ccb2057df85073e519a15ccc1">00349</a> vec <a class="code" href="classbdm_1_1egamma.html#457bfb1ccb2057df85073e519a15ccc1" title="Vector .">beta</a>;
303	<a name="l00350"></a>00350 <span class="keyword">public</span> :
304	<a name="l00353"></a>00353 <a class="code" href="classbdm_1_1egamma.html" title="Gamma posterior density.">egamma</a> ( ) :<a class="code" href="classbdm_1_1eEF.html" title="General conjugate exponential family posterior density.">eEF</a> ( ), <a class="code" href="classbdm_1_1egamma.html#0901ec983e66b8337aaa506e13b122fa" title="Vector .">alpha</a> ( 0 ), <a class="code" href="classbdm_1_1egamma.html#457bfb1ccb2057df85073e519a15ccc1" title="Vector .">beta</a> ( 0 ) {};
305	<a name="l00354"></a>00354 <a class="code" href="classbdm_1_1egamma.html" title="Gamma posterior density.">egamma</a> ( <span class="keyword">const</span> vec &a, <span class="keyword">const</span> vec &b ) {set_parameters ( a, b );};
306	<a name="l00355"></a>00355 <span class="keywordtype">void</span> set_parameters ( <span class="keyword">const</span> vec &a, <span class="keyword">const</span> vec &b ) {<a class="code" href="classbdm_1_1egamma.html#0901ec983e66b8337aaa506e13b122fa" title="Vector .">alpha</a>=a,<a class="code" href="classbdm_1_1egamma.html#457bfb1ccb2057df85073e519a15ccc1" title="Vector .">beta</a>=b;<a class="code" href="classbdm_1_1epdf.html#16adac20ec7fe07e1ea0b27d917788ce" title="dimension of the random variable">dim</a> = <a class="code" href="classbdm_1_1egamma.html#0901ec983e66b8337aaa506e13b122fa" title="Vector .">alpha</a>.length();};
307	<a name="l00357"></a>00357
308	<a name="l00358"></a>00358 vec <a class="code" href="classbdm_1_1egamma.html#6ed82f0fd05f6002487256d8e75a0bbd" title="Returns a sample, from density .">sample</a>() <span class="keyword">const</span>;
309	<a name="l00360"></a>00360 <span class="comment">// mat sample ( int N ) const;</span>
310	<a name="l00361"></a>00361 <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 &val ) <span class="keyword">const</span>;
311	<a name="l00362"></a>00362 <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>;
312	<a name="l00364"></a><a class="code" href="classbdm_1_1egamma.html#0865cb3d6339fdc7410806cf70a329ed">00364</a> vec& <a class="code" href="classbdm_1_1egamma.html#0865cb3d6339fdc7410806cf70a329ed" title="Returns poiter to alpha and beta. Potentially dengerous: use with care!">_alpha</a>() {<span class="keywordflow">return</span> <a class="code" href="classbdm_1_1egamma.html#0901ec983e66b8337aaa506e13b122fa" title="Vector .">alpha</a>;}
313	<a name="l00365"></a>00365 vec& _beta() {<span class="keywordflow">return</span> beta;}
314	<a name="l00366"></a><a class="code" href="classbdm_1_1egamma.html#49d256c42cce14c6faa56ec242b57e85">00366</a> vec <a class="code" href="classbdm_1_1egamma.html#49d256c42cce14c6faa56ec242b57e85" title="return expected value">mean</a>()<span class="keyword"> const </span>{<span class="keywordflow">return</span> elem_div ( <a class="code" href="classbdm_1_1egamma.html#0901ec983e66b8337aaa506e13b122fa" title="Vector .">alpha</a>,beta );}
315	<a name="l00367"></a><a class="code" href="classbdm_1_1egamma.html#36986cc01917cd0796fadc17125bdec1">00367</a> vec <a class="code" href="classbdm_1_1egamma.html#36986cc01917cd0796fadc17125bdec1" title="return expected variance (not covariance!)">variance</a>()<span class="keyword"> const </span>{<span class="keywordflow">return</span> elem_div ( <a class="code" href="classbdm_1_1egamma.html#0901ec983e66b8337aaa506e13b122fa" title="Vector .">alpha</a>,elem_mult ( beta,beta ) ); }
316	<a name="l00368"></a>00368 };
317	<a name="l00369"></a>00369
318	<a name="l00386"></a><a class="code" href="classbdm_1_1eigamma.html">00386</a> <span class="keyword">class </span><a class="code" href="classbdm_1_1eigamma.html" title="Inverse-Gamma posterior density.">eigamma</a> : <span class="keyword">public</span> <a class="code" href="classbdm_1_1egamma.html" title="Gamma posterior density.">egamma</a>
319	<a name="l00387"></a>00387 {
320	<a name="l00388"></a>00388 <span class="keyword">protected</span>:
321	<a name="l00389"></a>00389 <span class="keyword">public</span> :
322	<a name="l00394"></a>00394
323	<a name="l00395"></a><a class="code" href="classbdm_1_1eigamma.html#3aff7bf25ddac27731c60826fcfd878f">00395</a> vec <a class="code" href="classbdm_1_1eigamma.html#3aff7bf25ddac27731c60826fcfd878f" title="Returns a sample, from density .">sample</a>()<span class="keyword"> const </span>{<span class="keywordflow">return</span> 1.0/<a class="code" href="classbdm_1_1eigamma.html#3aff7bf25ddac27731c60826fcfd878f" title="Returns a sample, from density .">egamma::sample</a>();};
324	<a name="l00397"></a><a class="code" href="classbdm_1_1eigamma.html#46cecb295edbabd28120cb0f6f572bcb">00397</a> vec <a class="code" href="classbdm_1_1eigamma.html#46cecb295edbabd28120cb0f6f572bcb" title="Returns poiter to alpha and beta. Potentially dangerous: use with care!">mean</a>()<span class="keyword"> const </span>{<span class="keywordflow">return</span> elem_div ( <a class="code" href="classbdm_1_1egamma.html#457bfb1ccb2057df85073e519a15ccc1" title="Vector .">beta</a>,<a class="code" href="classbdm_1_1egamma.html#0901ec983e66b8337aaa506e13b122fa" title="Vector .">alpha</a>-1 );}
325	<a name="l00398"></a><a class="code" href="classbdm_1_1eigamma.html#c2c696f8c668e9f65392c9449f6a5133">00398</a> vec <a class="code" href="classbdm_1_1eigamma.html#c2c696f8c668e9f65392c9449f6a5133" title="return expected variance (not covariance!)">variance</a>()<span class="keyword"> const </span>{vec mea=<a class="code" href="classbdm_1_1eigamma.html#46cecb295edbabd28120cb0f6f572bcb" title="Returns poiter to alpha and beta. Potentially dangerous: use with care!">mean</a>(); <span class="keywordflow">return</span> elem_div ( elem_mult ( mea,mea ),<a class="code" href="classbdm_1_1egamma.html#0901ec983e66b8337aaa506e13b122fa" title="Vector .">alpha</a>-2 );}
326	<a name="l00399"></a>00399 };
327	<a name="l00400"></a>00400 <span class="comment">/*</span>
328	<a name="l00402"></a>00402 <span class="comment"> class emix : public epdf {</span>
329	<a name="l00403"></a>00403 <span class="comment"> protected:</span>
330	<a name="l00404"></a>00404 <span class="comment"> int n;</span>
331	<a name="l00405"></a>00405 <span class="comment"> vec &w;</span>
332	<a name="l00406"></a>00406 <span class="comment"> Array<epdf*> Coms;</span>
333	<a name="l00407"></a>00407 <span class="comment"> public:</span>
334	<a name="l00409"></a>00409 <span class="comment"> emix ( const RV &rv, vec &w0): epdf(rv), n(w0.length()), w(w0), Coms(n) {};</span>
335	<a name="l00410"></a>00410 <span class="comment"> void set_parameters( int &i, double wi, epdf* ep){w(i)=wi;Coms(i)=ep;}</span>
336	<a name="l00411"></a>00411 <span class="comment"> vec mean(){vec pom; for(int i=0;i<n;i++){pom+=Coms(i)->mean()*w(i);} return pom;};</span>
337	<a name="l00412"></a>00412 <span class="comment"> vec sample() {it_error ( "Not implemented" );return 0;}</span>
338	<a name="l00413"></a>00413 <span class="comment"> };</span>
339	<a name="l00414"></a>00414 <span class="comment"> */</span>
340	<a name="l00415"></a>00415
341	<a name="l00417"></a>00417
342	<a name="l00418"></a><a class="code" href="classbdm_1_1euni.html">00418</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>
343	<a name="l00419"></a>00419 {
344	<a name="l00420"></a>00420 <span class="keyword">protected</span>:
345	<a name="l00422"></a><a class="code" href="classbdm_1_1euni.html#ff7ce6a2ef5ef0015bbd1398bed12f32">00422</a> vec <a class="code" href="classbdm_1_1euni.html#ff7ce6a2ef5ef0015bbd1398bed12f32" title="lower bound on support">low</a>;
346	<a name="l00424"></a><a class="code" href="classbdm_1_1euni.html#cfad2dea4a62db6872bda8abd75f0de1">00424</a> vec <a class="code" href="classbdm_1_1euni.html#cfad2dea4a62db6872bda8abd75f0de1" title="upper bound on support">high</a>;
347	<a name="l00426"></a><a class="code" href="classbdm_1_1euni.html#d3c27e331f90c754d80228108de8ed4c">00426</a> vec <a class="code" href="classbdm_1_1euni.html#d3c27e331f90c754d80228108de8ed4c" title="internal">distance</a>;
348	<a name="l00428"></a><a class="code" href="classbdm_1_1euni.html#31bb13e8449a8eff35246d46dae35c20">00428</a> <span class="keywordtype">double</span> <a class="code" href="classbdm_1_1euni.html#31bb13e8449a8eff35246d46dae35c20" title="normalizing coefficients">nk</a>;
349	<a name="l00430"></a><a class="code" href="classbdm_1_1euni.html#3e63be48dd58659663ca60cd18700476">00430</a> <span class="keywordtype">double</span> <a class="code" href="classbdm_1_1euni.html#3e63be48dd58659663ca60cd18700476" title="cache of log( nk )">lnk</a>;
350	<a name="l00431"></a>00431 <span class="keyword">public</span>:
351	<a name="l00434"></a>00434 <a class="code" href="classbdm_1_1euni.html" title="Uniform distributed density on a rectangular support.">euni</a> ( ) :<a class="code" href="classbdm_1_1epdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a> ( ) {}
352	<a name="l00435"></a>00435 <a class="code" href="classbdm_1_1euni.html" title="Uniform distributed density on a rectangular support.">euni</a> ( <span class="keyword">const</span> vec &low0, <span class="keyword">const</span> vec &high0 ) {set_parameters ( low0,high0 );}
353	<a name="l00436"></a>00436 <span class="keywordtype">void</span> set_parameters ( <span class="keyword">const</span> vec &low0, <span class="keyword">const</span> vec &high0 )
354	<a name="l00437"></a>00437 {
355	<a name="l00438"></a>00438 <a class="code" href="classbdm_1_1euni.html#d3c27e331f90c754d80228108de8ed4c" title="internal">distance</a> = high0-low0;
356	<a name="l00439"></a>00439 it_assert_debug ( min ( <a class="code" href="classbdm_1_1euni.html#d3c27e331f90c754d80228108de8ed4c" title="internal">distance</a> ) >0.0,<span class="stringliteral">"bad support"</span> );
357	<a name="l00440"></a>00440 <a class="code" href="classbdm_1_1euni.html#ff7ce6a2ef5ef0015bbd1398bed12f32" title="lower bound on support">low</a> = low0;
358	<a name="l00441"></a>00441 <a class="code" href="classbdm_1_1euni.html#cfad2dea4a62db6872bda8abd75f0de1" title="upper bound on support">high</a> = high0;
359	<a name="l00442"></a>00442 <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> );
360	<a name="l00443"></a>00443 <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> );
361	<a name="l00444"></a>00444 <a class="code" href="classbdm_1_1epdf.html#16adac20ec7fe07e1ea0b27d917788ce" title="dimension of the random variable">dim</a> = <a class="code" href="classbdm_1_1euni.html#ff7ce6a2ef5ef0015bbd1398bed12f32" title="lower bound on support">low</a>.length();
362	<a name="l00445"></a>00445 }
363	<a name="l00447"></a>00447
364	<a name="l00448"></a>00448 <span class="keywordtype">double</span> eval ( <span class="keyword">const</span> vec &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>;}
365	<a name="l00449"></a><a class="code" href="classbdm_1_1euni.html#caa07b8307bd793d5339d6583e0aba81">00449</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 &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>;}
366	<a name="l00450"></a><a class="code" href="classbdm_1_1euni.html#fc5df80359ead2918384b2004ce67194">00450</a> vec <a class="code" href="classbdm_1_1euni.html#fc5df80359ead2918384b2004ce67194" title="Returns a sample, from density .">sample</a>()<span class="keyword"> const</span>
367	<a name="l00451"></a>00451 <span class="keyword"> </span>{
368	<a name="l00452"></a>00452 vec smp ( <a class="code" href="classbdm_1_1epdf.html#16adac20ec7fe07e1ea0b27d917788ce" title="dimension of the random variable">dim</a> );
369	<a name="l00453"></a>00453 <span class="preprocessor">#pragma omp critical</span>
370	<a name="l00454"></a>00454 <span class="preprocessor"></span> UniRNG.sample_vector ( <a class="code" href="classbdm_1_1epdf.html#16adac20ec7fe07e1ea0b27d917788ce" title="dimension of the random variable">dim</a> ,smp );
371	<a name="l00455"></a>00455 <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 );
372	<a name="l00456"></a>00456 }
373	<a name="l00458"></a><a class="code" href="classbdm_1_1euni.html#46caa8c13aba2e6228f964208918b226">00458</a> vec <a class="code" href="classbdm_1_1euni.html#46caa8c13aba2e6228f964208918b226" title="set values of low and high ">mean</a>()<span class="keyword"> const </span>{<span class="keywordflow">return</span> ( <a class="code" href="classbdm_1_1euni.html#cfad2dea4a62db6872bda8abd75f0de1" title="upper bound on support">high</a>-<a class="code" href="classbdm_1_1euni.html#ff7ce6a2ef5ef0015bbd1398bed12f32" title="lower bound on support">low</a> ) /2.0;}
374	<a name="l00459"></a><a class="code" href="classbdm_1_1euni.html#951f932155111f6053c980f672b4c22c">00459</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;}
375	<a name="l00460"></a>00460 };
376	<a name="l00461"></a>00461
377	<a name="l00462"></a>00462
378	<a name="l00468"></a>00468 <span class="keyword">template</span><<span class="keyword">class</span> sq_T>
379	<a name="l00469"></a><a class="code" href="classbdm_1_1mlnorm.html">00469</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>
380	<a name="l00470"></a>00470 {
381	<a name="l00471"></a>00471 <span class="keyword">protected</span>:
382	<a name="l00473"></a><a class="code" href="classbdm_1_1mlnorm.html#150ad6acb223b0a0abeaf92346686dcd">00473</a> <a class="code" href="classbdm_1_1enorm.html" title="Gaussian density with positive definite (decomposed) covariance matrix.">enorm<sq_T></a> <a class="code" href="classbdm_1_1epdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a>;
383	<a name="l00474"></a>00474 mat A;
384	<a name="l00475"></a>00475 vec mu_const;
385	<a name="l00476"></a>00476 vec& _mu; <span class="comment">//cached epdf.mu;</span>
386	<a name="l00477"></a>00477 <span class="keyword">public</span>:
387	<a name="l00480"></a>00480 <a class="code" href="classbdm_1_1mlnorm.html" title="Normal distributed linear function with linear function of mean value;.">mlnorm</a> ( ) :<a class="code" href="classbdm_1_1mEF.html" title="Exponential family model.">mEF</a> (),<a class="code" href="classbdm_1_1epdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a> ( ),A ( ),_mu ( <a class="code" href="classbdm_1_1epdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a>._mu() ) {<a class="code" href="classbdm_1_1mpdf.html#5eea43c56d38e4441bfb30270db949c0" title="pointer to internal epdf">ep</a> =&<a class="code" href="classbdm_1_1epdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a>; };
388	<a name="l00481"></a>00481 <a class="code" href="classbdm_1_1mlnorm.html" title="Normal distributed linear function with linear function of mean value;.">mlnorm</a> ( <span class="keyword">const</span> mat &A, <span class="keyword">const</span> vec &mu0, <span class="keyword">const</span> sq_T &R ) :<a class="code" href="classbdm_1_1epdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a> ( ),_mu ( <a class="code" href="classbdm_1_1epdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a>._mu() )
389	<a name="l00482"></a>00482 {
390	<a name="l00483"></a>00483 <a class="code" href="classbdm_1_1mpdf.html#5eea43c56d38e4441bfb30270db949c0" title="pointer to internal epdf">ep</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_1mlnorm.html#5d18dec3167584338a4775c1d165d96f" title="Set A and R.">set_parameters</a> ( A,mu0,R );
391	<a name="l00484"></a>00484 };
392	<a name="l00486"></a>00486 <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 &A, <span class="keyword">const</span> vec &mu0, <span class="keyword">const</span> sq_T &R );
393	<a name="l00489"></a>00489 <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1mlnorm.html#0dafc0196e7e07fd06dc6716e0e18bc2">condition</a> ( <span class="keyword">const</span> vec &cond );
394	<a name="l00490"></a>00490
395	<a name="l00492"></a><a class="code" href="classbdm_1_1mlnorm.html#56e587952f94fcac6cfc999eae6dbced">00492</a> vec& <a class="code" href="classbdm_1_1mlnorm.html#56e587952f94fcac6cfc999eae6dbced" title="access function">_mu_const</a>() {<span class="keywordflow">return</span> mu_const;}
396	<a name="l00494"></a><a class="code" href="classbdm_1_1mlnorm.html#262a2a486bff585f34bb6a5005b02614">00494</a> mat& <a class="code" href="classbdm_1_1mlnorm.html#262a2a486bff585f34bb6a5005b02614" title="access function">_A</a>() {<span class="keywordflow">return</span> A;}
397	<a name="l00496"></a><a class="code" href="classbdm_1_1mlnorm.html#78120ecd1c2b1d7e80124b4603504604">00496</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();}
398	<a name="l00497"></a>00497
399	<a name="l00498"></a>00498 <span class="keyword">template</span><<span class="keyword">class</span> sq_M>
400	<a name="l00499"></a>00499 <span class="keyword">friend</span> std::ostream &operator<< ( std::ostream &os, mlnorm<sq_M> &ml );
401	<a name="l00500"></a>00500 };
402	<a name="l00501"></a>00501
403	<a name="l00503"></a>00503 <span class="keyword">template</span><<span class="keyword">class</span> sq_T>
404	<a name="l00504"></a><a class="code" href="classbdm_1_1mgnorm.html">00504</a> <span class="keyword">class </span><a class="code" href="classbdm_1_1mgnorm.html" title="Mpdf with general function for mean value.">mgnorm</a> : <span class="keyword">public</span> <a class="code" href="classbdm_1_1mEF.html" title="Exponential family model.">mEF</a>
405	<a name="l00505"></a>00505 {
406	<a name="l00506"></a>00506 <span class="keyword">protected</span>:
407	<a name="l00508"></a><a class="code" href="classbdm_1_1mgnorm.html#8f7a376a1d2197e0634557e88e03104a">00508</a> <a class="code" href="classbdm_1_1enorm.html" title="Gaussian density with positive definite (decomposed) covariance matrix.">enorm<sq_T></a> <a class="code" href="classbdm_1_1epdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a>;
408	<a name="l00509"></a>00509 vec &mu;
409	<a name="l00510"></a>00510 <a class="code" href="classbdm_1_1fnc.html" title="Class representing function of variable represented by rv.">fnc</a>* g;
410	<a name="l00511"></a>00511 <span class="keyword">public</span>:
411	<a name="l00513"></a><a class="code" href="classbdm_1_1mgnorm.html#1b014915d74470d3efab74e07cacb97d">00513</a> <a class="code" href="classbdm_1_1mgnorm.html#1b014915d74470d3efab74e07cacb97d" title="default constructor">mgnorm</a>() :mu ( <a class="code" href="classbdm_1_1epdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a>._mu() ) {<a class="code" href="classbdm_1_1mpdf.html#5eea43c56d38e4441bfb30270db949c0" title="pointer to internal epdf">ep</a>=&<a class="code" href="classbdm_1_1epdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a>;}
412	<a name="l00515"></a><a class="code" href="classbdm_1_1mgnorm.html#578e02458e2a0d17f3864826b6ebd564">00515</a> <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1mgnorm.html#578e02458e2a0d17f3864826b6ebd564" title="set mean function">set_parameters</a> ( <a class="code" href="classbdm_1_1fnc.html" title="Class representing function of variable represented by rv.">fnc</a>* g0, <span class="keyword">const</span> sq_T &R0 ) {g=g0; <a class="code" href="classbdm_1_1epdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a>.set_parameters ( zeros ( g-><a class="code" href="classbdm_1_1fnc.html#083832294da9d1e40804158b979c4341" title="access function">dimension</a>() ), R0 );}
413	<a name="l00516"></a><a class="code" href="classbdm_1_1mgnorm.html#b31d63472cf6a1030cd8dbd8094c1f6d">00516</a> <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1mgnorm.html#b31d63472cf6a1030cd8dbd8094c1f6d" title="Update ep so that it represents this mpdf conditioned on rvc = cond.">condition</a> ( <span class="keyword">const</span> vec &cond ) {mu=g-><a class="code" href="classbdm_1_1fnc.html#6277b11d7fffc7ef8a2fa3e84ae5bad4" title="function evaluates numerical value of at cond ">eval</a> ( cond );};
414	<a name="l00517"></a>00517
415	<a name="l00518"></a>00518
416	<a name="l00546"></a><a class="code" href="classbdm_1_1mgnorm.html#d23d2c9b147c01785d5a4239c118ddf1">00546</a> <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1mgnorm.html#d23d2c9b147c01785d5a4239c118ddf1">from_setting</a>( <span class="keyword">const</span> Setting &root )
417	<a name="l00547"></a>00547 {
418	<a name="l00548"></a>00548 <a class="code" href="classbdm_1_1fnc.html" title="Class representing function of variable represented by rv.">fnc</a>* g = UI::build<fnc>( root, <span class="stringliteral">"g"</span> );
419	<a name="l00549"></a>00549
420	<a name="l00550"></a>00550 mat R;
421	<a name="l00551"></a>00551 <span class="keywordflow">if</span> ( root.exists( <span class="stringliteral">"dR"</span> ) )
422	<a name="l00552"></a>00552 {
423	<a name="l00553"></a>00553 vec dR;
424	<a name="l00554"></a>00554 <a class="code" href="classbdm_1_1UI.html#652bfd23f5052e4f1cb317057d74a3e2" title="This methods tries to build a new double matrix.">UI::get</a>( dR, root, <span class="stringliteral">"dR"</span> );
425	<a name="l00555"></a>00555 R=diag(dR);
426	<a name="l00556"></a>00556 }
427	<a name="l00557"></a>00557 <span class="keywordflow">else</span>
428	<a name="l00558"></a>00558 <a class="code" href="classbdm_1_1UI.html#652bfd23f5052e4f1cb317057d74a3e2" title="This methods tries to build a new double matrix.">UI::get</a>( R, root, <span class="stringliteral">"R"</span>);
429	<a name="l00559"></a>00559
430	<a name="l00560"></a>00560 <a class="code" href="classbdm_1_1mgnorm.html#578e02458e2a0d17f3864826b6ebd564" title="set mean function">set_parameters</a>(g,R);
431	<a name="l00561"></a>00561 }
432	<a name="l00562"></a>00562
433	<a name="l00563"></a>00563 <span class="comment">/*void mgnorm::to_setting( Setting &root ) const</span>
434	<a name="l00564"></a>00564 <span class="comment"> { </span>
435	<a name="l00565"></a>00565 <span class="comment"> Transport::to_setting( root );</span>
436	<a name="l00566"></a>00566 <span class="comment"></span>
437	<a name="l00567"></a>00567 <span class="comment"> Setting &kilometers_setting = root.add("kilometers", Setting::TypeInt );</span>
438	<a name="l00568"></a>00568 <span class="comment"> kilometers_setting = kilometers;</span>
439	<a name="l00569"></a>00569 <span class="comment"></span>
440	<a name="l00570"></a>00570 <span class="comment"> UI::save( passengers, root, "passengers" );</span>
441	<a name="l00571"></a>00571 <span class="comment"> }*/</span>
442	<a name="l00572"></a>00572
443	<a name="l00573"></a>00573 };
444	<a name="l00574"></a>00574
445	<a name="l00575"></a>00575 UIREGISTER(mgnorm<chmat>);
446	<a name="l00576"></a>00576
447	<a name="l00577"></a>00577
448	<a name="l00585"></a><a class="code" href="classbdm_1_1mlstudent.html">00585</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><ldmat>
449	<a name="l00586"></a>00586 {
450	<a name="l00587"></a>00587 <span class="keyword">protected</span>:
451	<a name="l00588"></a>00588 <a class="code" href="classldmat.html" title="Matrix stored in LD form, (commonly known as UD).">ldmat</a> Lambda;
452	<a name="l00589"></a>00589 <a class="code" href="classldmat.html" title="Matrix stored in LD form, (commonly known as UD).">ldmat</a> &<a class="code" href="classbdm_1_1mlnorm.html#78120ecd1c2b1d7e80124b4603504604" title="access function">_R</a>;
453	<a name="l00590"></a>00590 <a class="code" href="classldmat.html" title="Matrix stored in LD form, (commonly known as UD).">ldmat</a> Re;
454	<a name="l00591"></a>00591 <span class="keyword">public</span>:
455	<a name="l00592"></a>00592 <a class="code" href="classbdm_1_1mlstudent.html">mlstudent</a> ( ) :<a class="code" href="classbdm_1_1mlnorm.html">mlnorm<ldmat></a> (),
456	<a name="l00593"></a>00593 Lambda (), <a class="code" href="classbdm_1_1mlnorm.html#78120ecd1c2b1d7e80124b4603504604" title="access function">_R</a> ( <a class="code" href="classbdm_1_1epdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a>._R() ) {}
457	<a name="l00594"></a>00594 <span class="keywordtype">void</span> set_parameters ( <span class="keyword">const</span> mat &A0, <span class="keyword">const</span> vec &mu0, <span class="keyword">const</span> <a class="code" href="classldmat.html" title="Matrix stored in LD form, (commonly known as UD).">ldmat</a> &R0, <span class="keyword">const</span> <a class="code" href="classldmat.html" title="Matrix stored in LD form, (commonly known as UD).">ldmat</a>& Lambda0 )
458	<a name="l00595"></a>00595 {
459	<a name="l00596"></a>00596 it_assert_debug ( A0.rows() ==mu0.length(),<span class="stringliteral">""</span> );
460	<a name="l00597"></a>00597 it_assert_debug ( R0.<a class="code" href="group__math.html#g96dfb21865db4f5bd36fa70f9b0b1163" title="access function">rows</a>() ==A0.rows(),<span class="stringliteral">""</span> );
461	<a name="l00598"></a>00598
462	<a name="l00599"></a>00599 <a class="code" href="classbdm_1_1epdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a>.set_parameters ( mu0,Lambda ); <span class="comment">//</span>
463	<a name="l00600"></a>00600 A = A0;
464	<a name="l00601"></a>00601 mu_const = mu0;
465	<a name="l00602"></a>00602 Re=R0;
466	<a name="l00603"></a>00603 Lambda = Lambda0;
467	<a name="l00604"></a>00604 }
468	<a name="l00605"></a><a class="code" href="classbdm_1_1mlstudent.html#efd37560585c8613897f30d3c2f58d0d">00605</a> <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1mlstudent.html#efd37560585c8613897f30d3c2f58d0d">condition</a> ( <span class="keyword">const</span> vec &cond )
469	<a name="l00606"></a>00606 {
470	<a name="l00607"></a>00607 _mu = A*cond + mu_const;
471	<a name="l00608"></a>00608 <span class="keywordtype">double</span> zeta;
472	<a name="l00609"></a>00609 <span class="comment">//ugly hack!</span>
473	<a name="l00610"></a>00610 <span class="keywordflow">if</span> ( ( cond.length() +1 ) ==Lambda.<a class="code" href="group__math.html#g96dfb21865db4f5bd36fa70f9b0b1163" title="access function">rows</a>() )
474	<a name="l00611"></a>00611 {
475	<a name="l00612"></a>00612 zeta = Lambda.<a class="code" href="classldmat.html#d876c5f83e02b3e809b35c9de5068f14" title="Evaluates quadratic form ;.">invqform</a> ( concat ( cond, vec_1 ( 1.0 ) ) );
476	<a name="l00613"></a>00613 }
477	<a name="l00614"></a>00614 <span class="keywordflow">else</span>
478	<a name="l00615"></a>00615 {
479	<a name="l00616"></a>00616 zeta = Lambda.<a class="code" href="classldmat.html#d876c5f83e02b3e809b35c9de5068f14" title="Evaluates quadratic form ;.">invqform</a> ( cond );
480	<a name="l00617"></a>00617 }
481	<a name="l00618"></a>00618 <a class="code" href="classbdm_1_1mlnorm.html#78120ecd1c2b1d7e80124b4603504604" title="access function">_R</a> = Re;
482	<a name="l00619"></a>00619 <a class="code" href="classbdm_1_1mlnorm.html#78120ecd1c2b1d7e80124b4603504604" title="access function">_R</a>*= ( 1+zeta );<span class="comment">// / ( nu ); << nu is in Re!!!!!!</span>
483	<a name="l00620"></a>00620 };
484	<a name="l00621"></a>00621
485	<a name="l00622"></a>00622 };
486	<a name="l00632"></a><a class="code" href="classbdm_1_1mgamma.html">00632</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>
487	<a name="l00633"></a>00633 {
488	<a name="l00634"></a>00634 <span class="keyword">protected</span>:
489	<a name="l00636"></a><a class="code" href="classbdm_1_1mgamma.html#bdc9f1e9e03c09e91103fee269864438">00636</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>;
490	<a name="l00638"></a><a class="code" href="classbdm_1_1mgamma.html#b20cf88cca1fe9b0b8f2a412608bfd09">00638</a> <span class="keywordtype">double</span> <a class="code" href="classbdm_1_1mgamma.html#b20cf88cca1fe9b0b8f2a412608bfd09" title="Constant .">k</a>;
491	<a name="l00640"></a><a class="code" href="classbdm_1_1mgamma.html#3d95f4dde9214ff6dba265e18af60312">00640</a> vec &<a class="code" href="classbdm_1_1mgamma.html#3d95f4dde9214ff6dba265e18af60312" title="cache of epdf.beta">_beta</a>;
492	<a name="l00641"></a>00641
493	<a name="l00642"></a>00642 <span class="keyword">public</span>:
494	<a name="l00644"></a><a class="code" href="classbdm_1_1mgamma.html#1a9dc8661e5b214a8185d6e6b9956eb1">00644</a> <a class="code" href="classbdm_1_1mgamma.html#1a9dc8661e5b214a8185d6e6b9956eb1" title="Constructor.">mgamma</a> ( ) : <a class="code" href="classbdm_1_1mEF.html" title="Exponential family model.">mEF</a> ( ), <a class="code" href="classbdm_1_1epdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a> (), <a class="code" href="classbdm_1_1mgamma.html#3d95f4dde9214ff6dba265e18af60312" title="cache of epdf.beta">_beta</a> ( <a class="code" href="classbdm_1_1epdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a>.<a class="code" href="classbdm_1_1mgamma.html#3d95f4dde9214ff6dba265e18af60312" title="cache of epdf.beta">_beta</a>() ) {<a class="code" href="classbdm_1_1mpdf.html#5eea43c56d38e4441bfb30270db949c0" title="pointer to internal epdf">ep</a>=&<a class="code" href="classbdm_1_1epdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a>;};
495	<a name="l00646"></a>00646 <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1mgamma.html#a0f21c2557b233a85838b497d040ab14" title="Set value of k.">set_parameters</a> ( <span class="keywordtype">double</span> <a class="code" href="classbdm_1_1mgamma.html#b20cf88cca1fe9b0b8f2a412608bfd09" title="Constant .">k</a>, <span class="keyword">const</span> vec &beta0 );
496	<a name="l00647"></a><a class="code" href="classbdm_1_1mgamma.html#8996500f1885e39cde30221b20900bff">00647</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 &val ) {<a class="code" href="classbdm_1_1mgamma.html#3d95f4dde9214ff6dba265e18af60312" title="cache of epdf.beta">_beta</a>=<a class="code" href="classbdm_1_1mgamma.html#b20cf88cca1fe9b0b8f2a412608bfd09" title="Constant .">k</a>/val;};
497	<a name="l00648"></a>00648 };
498	<a name="l00649"></a>00649
499	<a name="l00659"></a><a class="code" href="classbdm_1_1migamma.html">00659</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>
500	<a name="l00660"></a>00660 {
501	<a name="l00661"></a>00661 <span class="keyword">protected</span>:
502	<a name="l00663"></a><a class="code" href="classbdm_1_1migamma.html#a31b39d4179551b593c9e0d7d756783a">00663</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>;
503	<a name="l00665"></a><a class="code" href="classbdm_1_1migamma.html#dc56bc9da542e0103ec16b9be8e5e38c">00665</a> <span class="keywordtype">double</span> <a class="code" href="classbdm_1_1migamma.html#dc56bc9da542e0103ec16b9be8e5e38c" title="Constant .">k</a>;
504	<a name="l00667"></a><a class="code" href="classbdm_1_1migamma.html#c9847093da59a9ba0ebb68d2c592f5dc">00667</a> vec &<a class="code" href="classbdm_1_1migamma.html#c9847093da59a9ba0ebb68d2c592f5dc" title="cache of epdf.alpha">_alpha</a>;
505	<a name="l00669"></a><a class="code" href="classbdm_1_1migamma.html#0d854c047001b5465cf1ba21f52904b5">00669</a> vec &<a class="code" href="classbdm_1_1migamma.html#0d854c047001b5465cf1ba21f52904b5" title="cache of epdf.beta">_beta</a>;
506	<a name="l00670"></a>00670
507	<a name="l00671"></a>00671 <span class="keyword">public</span>:
508	<a name="l00674"></a>00674 <a class="code" href="classbdm_1_1migamma.html" title="Inverse-Gamma random walk.">migamma</a> ( ) : <a class="code" href="classbdm_1_1mEF.html" title="Exponential family model.">mEF</a> (), <a class="code" href="classbdm_1_1epdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a> ( ), <a class="code" href="classbdm_1_1migamma.html#c9847093da59a9ba0ebb68d2c592f5dc" title="cache of epdf.alpha">_alpha</a> ( <a class="code" href="classbdm_1_1epdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a>.<a class="code" href="classbdm_1_1migamma.html#c9847093da59a9ba0ebb68d2c592f5dc" title="cache of epdf.alpha">_alpha</a>() ), <a class="code" href="classbdm_1_1migamma.html#0d854c047001b5465cf1ba21f52904b5" title="cache of epdf.beta">_beta</a> ( <a class="code" href="classbdm_1_1epdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a>.<a class="code" href="classbdm_1_1migamma.html#0d854c047001b5465cf1ba21f52904b5" title="cache of epdf.beta">_beta</a>() ) {<a class="code" href="classbdm_1_1mpdf.html#5eea43c56d38e4441bfb30270db949c0" title="pointer to internal epdf">ep</a>=&<a class="code" href="classbdm_1_1epdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a>;};
509	<a name="l00675"></a>00675 <a class="code" href="classbdm_1_1migamma.html" title="Inverse-Gamma random walk.">migamma</a> ( <span class="keyword">const</span> <a class="code" href="classbdm_1_1migamma.html" title="Inverse-Gamma random walk.">migamma</a> &m ) : <a class="code" href="classbdm_1_1mEF.html" title="Exponential family model.">mEF</a> (), <a class="code" href="classbdm_1_1epdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a> ( m.<a class="code" href="classbdm_1_1epdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a> ), <a class="code" href="classbdm_1_1migamma.html#c9847093da59a9ba0ebb68d2c592f5dc" title="cache of epdf.alpha">_alpha</a> ( <a class="code" href="classbdm_1_1epdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a>.<a class="code" href="classbdm_1_1migamma.html#c9847093da59a9ba0ebb68d2c592f5dc" title="cache of epdf.alpha">_alpha</a>() ), <a class="code" href="classbdm_1_1migamma.html#0d854c047001b5465cf1ba21f52904b5" title="cache of epdf.beta">_beta</a> ( <a class="code" href="classbdm_1_1epdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a>.<a class="code" href="classbdm_1_1migamma.html#0d854c047001b5465cf1ba21f52904b5" title="cache of epdf.beta">_beta</a>() ) {<a class="code" href="classbdm_1_1mpdf.html#5eea43c56d38e4441bfb30270db949c0" title="pointer to internal epdf">ep</a>=&<a class="code" href="classbdm_1_1epdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a>;};
510	<a name="l00677"></a>00677
511	<a name="l00679"></a><a class="code" href="classbdm_1_1migamma.html#8b10ab922e2a7bae2fb6bb3efc7b6151">00679</a> <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1migamma.html#8b10ab922e2a7bae2fb6bb3efc7b6151" title="Set value of k.">set_parameters</a> ( <span class="keywordtype">int</span> len, <span class="keywordtype">double</span> k0 )
512	<a name="l00680"></a>00680 {
513	<a name="l00681"></a>00681 <a class="code" href="classbdm_1_1migamma.html#dc56bc9da542e0103ec16b9be8e5e38c" title="Constant .">k</a>=k0;
514	<a name="l00682"></a>00682 <a class="code" href="classbdm_1_1epdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a>.set_parameters ( ( 1.0/ ( <a class="code" href="classbdm_1_1migamma.html#dc56bc9da542e0103ec16b9be8e5e38c" title="Constant .">k</a><a class="code" href="classbdm_1_1migamma.html#dc56bc9da542e0103ec16b9be8e5e38c" title="Constant .">k</a> ) +2.0 ) ones ( len ) <span class="comment">/alpha/</span>, ones ( len ) <span class="comment">/beta/</span> );
515	<a name="l00683"></a>00683 <a class="code" href="classbdm_1_1mpdf.html#7c1900976ff13dbc09c9729b3bbff9e6" title="dimension of the condition">dimc</a> = dimension();
516	<a name="l00684"></a>00684 };
517	<a name="l00685"></a><a class="code" href="classbdm_1_1migamma.html#7a34b1e2e3aa2250d7c0ed7df1665b8c">00685</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 &val )
518	<a name="l00686"></a>00686 {
519	<a name="l00687"></a>00687 <a class="code" href="classbdm_1_1migamma.html#0d854c047001b5465cf1ba21f52904b5" title="cache of epdf.beta">_beta</a>=elem_mult ( val, ( <a class="code" href="classbdm_1_1migamma.html#c9847093da59a9ba0ebb68d2c592f5dc" title="cache of epdf.alpha">_alpha</a>-1.0 ) );
520	<a name="l00688"></a>00688 };
521	<a name="l00689"></a>00689 };
522	<a name="l00690"></a>00690
523	<a name="l00691"></a>00691
524	<a name="l00703"></a><a class="code" href="classbdm_1_1mgamma__fix.html">00703</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>
525	<a name="l00704"></a>00704 {
526	<a name="l00705"></a>00705 <span class="keyword">protected</span>:
527	<a name="l00707"></a><a class="code" href="classbdm_1_1mgamma__fix.html#1eb701506aabb2e6af007e487212d6fa">00707</a> <span class="keywordtype">double</span> <a class="code" href="classbdm_1_1mgamma__fix.html#1eb701506aabb2e6af007e487212d6fa" title="parameter l">l</a>;
528	<a name="l00709"></a><a class="code" href="classbdm_1_1mgamma__fix.html#018c6f901a04e419455308a07eb3b0b2">00709</a> vec <a class="code" href="classbdm_1_1mgamma__fix.html#018c6f901a04e419455308a07eb3b0b2" title="reference vector">refl</a>;
529	<a name="l00710"></a>00710 <span class="keyword">public</span>:
530	<a name="l00712"></a><a class="code" href="classbdm_1_1mgamma__fix.html#9a31bc9b4b60188a18a2a6b588dc4b2d">00712</a> <a class="code" href="classbdm_1_1mgamma__fix.html#9a31bc9b4b60188a18a2a6b588dc4b2d" title="Constructor.">mgamma_fix</a> ( ) : <a class="code" href="classbdm_1_1mgamma.html" title="Gamma random walk.">mgamma</a> ( ),<a class="code" href="classbdm_1_1mgamma__fix.html#018c6f901a04e419455308a07eb3b0b2" title="reference vector">refl</a> () {};
531	<a name="l00714"></a><a class="code" href="classbdm_1_1mgamma__fix.html#1bfd30e90db9dc1fbda4a9fbb0b716b2">00714</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 )
532	<a name="l00715"></a>00715 {
533	<a name="l00716"></a>00716 <a class="code" href="classbdm_1_1mgamma__fix.html#1bfd30e90db9dc1fbda4a9fbb0b716b2" title="Set value of k.">mgamma::set_parameters</a> ( k0, ref0 );
534	<a name="l00717"></a>00717 <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;
535	<a name="l00718"></a>00718 <a class="code" href="classbdm_1_1mpdf.html#7c1900976ff13dbc09c9729b3bbff9e6" title="dimension of the condition">dimc</a>=dimension();
536	<a name="l00719"></a>00719 };
537	<a name="l00720"></a>00720
538	<a name="l00721"></a><a class="code" href="classbdm_1_1mgamma__fix.html#1d539591deb7a38bb3403c2b396c8ff7">00721</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 &val ) {vec mean=elem_mult ( <a class="code" href="classbdm_1_1mgamma__fix.html#018c6f901a04e419455308a07eb3b0b2" title="reference vector">refl</a>,pow ( val,<a class="code" href="classbdm_1_1mgamma__fix.html#1eb701506aabb2e6af007e487212d6fa" title="parameter l">l</a> ) ); <a class="code" href="classbdm_1_1mgamma.html#3d95f4dde9214ff6dba265e18af60312" title="cache of epdf.beta">_beta</a>=<a class="code" href="classbdm_1_1mgamma.html#b20cf88cca1fe9b0b8f2a412608bfd09" title="Constant .">k</a>/mean;};
539	<a name="l00722"></a>00722 };
540	<a name="l00723"></a>00723
541	<a name="l00724"></a>00724
542	<a name="l00737"></a><a class="code" href="classbdm_1_1migamma__ref.html">00737</a> <span class="keyword">class </span><a class="code" href="classbdm_1_1migamma__ref.html" title="Inverse-Gamma random walk around a fixed point.">migamma_ref</a> : <span class="keyword">public</span> <a class="code" href="classbdm_1_1migamma.html" title="Inverse-Gamma random walk.">migamma</a>
543	<a name="l00738"></a>00738 {
544	<a name="l00739"></a>00739 <span class="keyword">protected</span>:
545	<a name="l00741"></a><a class="code" href="classbdm_1_1migamma__ref.html#cdc1345ba8375fbdb18a69322d2f841d">00741</a> <span class="keywordtype">double</span> <a class="code" href="classbdm_1_1migamma__ref.html#cdc1345ba8375fbdb18a69322d2f841d" title="parameter l">l</a>;
546	<a name="l00743"></a><a class="code" href="classbdm_1_1migamma__ref.html#3692dc67caf4367e15564d37f45476f6">00743</a> vec <a class="code" href="classbdm_1_1migamma__ref.html#3692dc67caf4367e15564d37f45476f6" title="reference vector">refl</a>;
547	<a name="l00744"></a>00744 <span class="keyword">public</span>:
548	<a name="l00746"></a><a class="code" href="classbdm_1_1migamma__ref.html#f45b15a10f084991ba6b48295f10421f">00746</a> <a class="code" href="classbdm_1_1migamma__ref.html#f45b15a10f084991ba6b48295f10421f" title="Constructor.">migamma_ref</a> ( ) : <a class="code" href="classbdm_1_1migamma.html" title="Inverse-Gamma random walk.">migamma</a> (),<a class="code" href="classbdm_1_1migamma__ref.html#3692dc67caf4367e15564d37f45476f6" title="reference vector">refl</a> ( ) {};
549	<a name="l00748"></a><a class="code" href="classbdm_1_1migamma__ref.html#b0b4eb278ef5d0831ec4954ba7bd2800">00748</a> <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1migamma__ref.html#b0b4eb278ef5d0831ec4954ba7bd2800" title="Set value of k.">set_parameters</a> ( <span class="keywordtype">double</span> k0 , vec ref0, <span class="keywordtype">double</span> l0 )
550	<a name="l00749"></a>00749 {
551	<a name="l00750"></a>00750 <a class="code" href="classbdm_1_1migamma__ref.html#b0b4eb278ef5d0831ec4954ba7bd2800" title="Set value of k.">migamma::set_parameters</a> ( ref0.length(), k0 );
552	<a name="l00751"></a>00751 <a class="code" href="classbdm_1_1migamma__ref.html#3692dc67caf4367e15564d37f45476f6" title="reference vector">refl</a>=pow ( ref0,1.0-l0 );
553	<a name="l00752"></a>00752 <a class="code" href="classbdm_1_1migamma__ref.html#cdc1345ba8375fbdb18a69322d2f841d" title="parameter l">l</a>=l0;
554	<a name="l00753"></a>00753 <a class="code" href="classbdm_1_1mpdf.html#7c1900976ff13dbc09c9729b3bbff9e6" title="dimension of the condition">dimc</a> = dimension();
555	<a name="l00754"></a>00754 };
556	<a name="l00755"></a>00755
557	<a name="l00756"></a><a class="code" href="classbdm_1_1migamma__ref.html#ae86b2e4ff963d62e05d4e130514634a">00756</a> <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1migamma__ref.html#ae86b2e4ff963d62e05d4e130514634a" title="Update ep so that it represents this mpdf conditioned on rvc = cond.">condition</a> ( <span class="keyword">const</span> vec &val )
558	<a name="l00757"></a>00757 {
559	<a name="l00758"></a>00758 vec mean=elem_mult ( <a class="code" href="classbdm_1_1migamma__ref.html#3692dc67caf4367e15564d37f45476f6" title="reference vector">refl</a>,pow ( val,<a class="code" href="classbdm_1_1migamma__ref.html#cdc1345ba8375fbdb18a69322d2f841d" title="parameter l">l</a> ) );
560	<a name="l00759"></a>00759 <a class="code" href="classbdm_1_1migamma__ref.html#ae86b2e4ff963d62e05d4e130514634a" title="Update ep so that it represents this mpdf conditioned on rvc = cond.">migamma::condition</a> ( mean );
561	<a name="l00760"></a>00760 };
562	<a name="l00761"></a>00761
563	<a name="l00782"></a>00782 <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1migamma__ref.html#6ab1cf56dd7b718c285ee65d8953cf3e">from_setting</a>( <span class="keyword">const</span> Setting &root );
564	<a name="l00783"></a>00783
565	<a name="l00784"></a>00784 <span class="comment">// TODO dodelat void to_setting( Setting &root ) const;</span>
566	<a name="l00785"></a>00785 };
567	<a name="l00786"></a>00786
568	<a name="l00787"></a>00787
569	<a name="l00788"></a>00788 UIREGISTER(migamma_ref);
570	<a name="l00789"></a>00789
571	<a name="l00799"></a><a class="code" href="classbdm_1_1elognorm.html">00799</a> <span class="keyword">class </span><a class="code" href="classbdm_1_1elognorm.html">elognorm</a>: <span class="keyword">public</span> <a class="code" href="classbdm_1_1enorm.html" title="Gaussian density with positive definite (decomposed) covariance matrix.">enorm</a><ldmat>
572	<a name="l00800"></a>00800 {
573	<a name="l00801"></a>00801 <span class="keyword">public</span>:
574	<a name="l00802"></a><a class="code" href="classbdm_1_1elognorm.html#8b948e2bce1253765a2542199913aaba">00802</a> vec <a class="code" href="classbdm_1_1elognorm.html#8b948e2bce1253765a2542199913aaba" title="Returns a sample, from density .">sample</a>()<span class="keyword"> const </span>{<span class="keywordflow">return</span> exp ( <a class="code" href="classbdm_1_1enorm.html" title="Gaussian density with positive definite (decomposed) covariance matrix.">enorm<ldmat>::sample</a>() );};
575	<a name="l00803"></a><a class="code" href="classbdm_1_1elognorm.html#adb41e4f4d6600dec6f8c1dbc5ed9eea">00803</a> vec <a class="code" href="classbdm_1_1elognorm.html#adb41e4f4d6600dec6f8c1dbc5ed9eea" title="return expected value">mean</a>()<span class="keyword"> const </span>{vec var=<a class="code" href="classbdm_1_1enorm.html#729c75ef0fa8abae03d58ad1f81e6773" title="return expected variance (not covariance!)">enorm<ldmat>::variance</a>();<span class="keywordflow">return</span> exp ( <a class="code" href="classbdm_1_1enorm.html#c702a194720853570d08b65482f842c7" title="mean value">mu</a> - 0.5*var );};
576	<a name="l00804"></a>00804
577	<a name="l00805"></a>00805 };
578	<a name="l00806"></a>00806
579	<a name="l00818"></a><a class="code" href="classbdm_1_1mlognorm.html">00818</a> <span class="keyword">class </span><a class="code" href="classbdm_1_1mlognorm.html" title="Log-Normal random walk.">mlognorm</a> : <span class="keyword">public</span> <a class="code" href="classbdm_1_1mpdf.html" title="Conditional probability density, e.g. modeling some dependencies.">mpdf</a>
580	<a name="l00819"></a>00819 {
581	<a name="l00820"></a>00820 <span class="keyword">protected</span>:
582	<a name="l00821"></a>00821 <a class="code" href="classbdm_1_1elognorm.html">elognorm</a> eno;
583	<a name="l00823"></a><a class="code" href="classbdm_1_1mlognorm.html#a51128a2e503b8b2ce698244b9e0db1a">00823</a> <span class="keywordtype">double</span> <a class="code" href="classbdm_1_1mlognorm.html#a51128a2e503b8b2ce698244b9e0db1a" title="parameter 1/2*sigma^2">sig2</a>;
584	<a name="l00825"></a><a class="code" href="classbdm_1_1mlognorm.html#7d0063f77d899ef22e8c5edd642176d2">00825</a> vec &<a class="code" href="classbdm_1_1mlognorm.html#7d0063f77d899ef22e8c5edd642176d2" title="access">mu</a>;
585	<a name="l00826"></a>00826 <span class="keyword">public</span>:
586	<a name="l00828"></a><a class="code" href="classbdm_1_1mlognorm.html#a5d6eb2688d02e0348b96c4fbd7bde41">00828</a> <a class="code" href="classbdm_1_1mlognorm.html#a5d6eb2688d02e0348b96c4fbd7bde41" title="Constructor.">mlognorm</a> ( ) : eno (), <a class="code" href="classbdm_1_1mlognorm.html#7d0063f77d899ef22e8c5edd642176d2" title="access">mu</a> ( eno._mu() ) {<a class="code" href="classbdm_1_1mpdf.html#5eea43c56d38e4441bfb30270db949c0" title="pointer to internal epdf">ep</a>=&eno;};
587	<a name="l00830"></a><a class="code" href="classbdm_1_1mlognorm.html#604cab0e8a76f9041dc3c606043bb39f">00830</a> <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1mlognorm.html#604cab0e8a76f9041dc3c606043bb39f" title="Set value of k.">set_parameters</a> ( <span class="keywordtype">int</span> size, <span class="keywordtype">double</span> k )
588	<a name="l00831"></a>00831 {
589	<a name="l00832"></a>00832 <a class="code" href="classbdm_1_1mlognorm.html#a51128a2e503b8b2ce698244b9e0db1a" title="parameter 1/2sigma^2">sig2</a> = 0.5log ( k*k+1 );
590	<a name="l00833"></a>00833 eno.<a class="code" href="classbdm_1_1enorm.html#b8322f2c11560871dd922c660f4771bb">set_parameters</a> ( zeros ( size ),2<a class="code" href="classbdm_1_1mlognorm.html#a51128a2e503b8b2ce698244b9e0db1a" title="parameter 1/2sigma^2">sig2</a>*eye ( size ) );
591	<a name="l00834"></a>00834
592	<a name="l00835"></a>00835 <a class="code" href="classbdm_1_1mpdf.html#7c1900976ff13dbc09c9729b3bbff9e6" title="dimension of the condition">dimc</a> = size;
593	<a name="l00836"></a>00836 };
594	<a name="l00837"></a>00837
595	<a name="l00838"></a><a class="code" href="classbdm_1_1mlognorm.html#9106d8fd8bdf2b6be675ffd8f3ca584e">00838</a> <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1mlognorm.html#9106d8fd8bdf2b6be675ffd8f3ca584e" title="Update ep so that it represents this mpdf conditioned on rvc = cond.">condition</a> ( <span class="keyword">const</span> vec &val )
596	<a name="l00839"></a>00839 {
597	<a name="l00840"></a>00840 <a class="code" href="classbdm_1_1mlognorm.html#7d0063f77d899ef22e8c5edd642176d2" title="access">mu</a>=log ( val )-<a class="code" href="classbdm_1_1mlognorm.html#a51128a2e503b8b2ce698244b9e0db1a" title="parameter 1/2*sigma^2">sig2</a>;<span class="comment">//elem_mult ( refl,pow ( val,l ) );</span>
598	<a name="l00841"></a>00841 };
599	<a name="l00842"></a>00842
600	<a name="l00861"></a>00861 <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1mlognorm.html#3a130457942be64ee9544e8dff00d09b">from_setting</a>( <span class="keyword">const</span> Setting &root );
601	<a name="l00862"></a>00862
602	<a name="l00863"></a>00863 <span class="comment">// TODO dodelat void to_setting( Setting &root ) const;</span>
603	<a name="l00864"></a>00864
604	<a name="l00865"></a>00865 };
605	<a name="l00866"></a>00866
606	<a name="l00867"></a>00867 UIREGISTER(mlognorm);
607	<a name="l00868"></a>00868
608	<a name="l00872"></a><a class="code" href="classbdm_1_1eWishartCh.html">00872</a> <span class="keyword">class </span><a class="code" href="classbdm_1_1eWishartCh.html">eWishartCh</a> : <span class="keyword">public</span> <a class="code" href="classbdm_1_1epdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a>
609	<a name="l00873"></a>00873 {
610	<a name="l00874"></a>00874 <span class="keyword">protected</span>:
611	<a name="l00876"></a><a class="code" href="classbdm_1_1eWishartCh.html#1b42f9284a32f23b0b253a628cda7490">00876</a> <a class="code" href="classchmat.html" title="Symmetric matrix stored in square root decomposition using upper cholesky.">chmat</a> <a class="code" href="classbdm_1_1eWishartCh.html#1b42f9284a32f23b0b253a628cda7490" title="Upper-Triagle of Choleski decomposition of .">Y</a>;
612	<a name="l00878"></a><a class="code" href="classbdm_1_1eWishartCh.html#b745c73faef785009484180582050a1f">00878</a> <span class="keywordtype">int</span> <a class="code" href="classbdm_1_1eWishartCh.html#b745c73faef785009484180582050a1f" title="dimension of matrix ">p</a>;
613	<a name="l00880"></a><a class="code" href="classbdm_1_1eWishartCh.html#1879a14d7d2bb05062523b189baa11c3">00880</a> <span class="keywordtype">double</span> <a class="code" href="classbdm_1_1eWishartCh.html#1879a14d7d2bb05062523b189baa11c3" title="degrees of freedom ">delta</a>;
614	<a name="l00881"></a>00881 <span class="keyword">public</span>:
615	<a name="l00882"></a>00882 <span class="keywordtype">void</span> set_parameters ( <span class="keyword">const</span> mat &Y0, <span class="keyword">const</span> <span class="keywordtype">double</span> delta0 ) {<a class="code" href="classbdm_1_1eWishartCh.html#1b42f9284a32f23b0b253a628cda7490" title="Upper-Triagle of Choleski decomposition of .">Y</a>=<a class="code" href="classchmat.html" title="Symmetric matrix stored in square root decomposition using upper cholesky.">chmat</a> ( Y0 );<a class="code" href="classbdm_1_1eWishartCh.html#1879a14d7d2bb05062523b189baa11c3" title="degrees of freedom ">delta</a>=delta0; <a class="code" href="classbdm_1_1eWishartCh.html#b745c73faef785009484180582050a1f" title="dimension of matrix ">p</a>=<a class="code" href="classbdm_1_1eWishartCh.html#1b42f9284a32f23b0b253a628cda7490" title="Upper-Triagle of Choleski decomposition of .">Y</a>.<a class="code" href="classsqmat.html#071e80ced9cc3b8cbb360fa7462eb646" title="Reimplementing common functions of mat: cols().">rows</a>(); <a class="code" href="classbdm_1_1epdf.html#16adac20ec7fe07e1ea0b27d917788ce" title="dimension of the random variable">dim</a> = <a class="code" href="classbdm_1_1eWishartCh.html#b745c73faef785009484180582050a1f" title="dimension of matrix ">p</a>*<a class="code" href="classbdm_1_1eWishartCh.html#b745c73faef785009484180582050a1f" title="dimension of matrix ">p</a>; }
616	<a name="l00883"></a>00883 mat sample_mat()<span class="keyword"> const</span>
617	<a name="l00884"></a>00884 <span class="keyword"> </span>{
618	<a name="l00885"></a>00885 mat X=zeros ( <a class="code" href="classbdm_1_1eWishartCh.html#b745c73faef785009484180582050a1f" title="dimension of matrix ">p</a>,<a class="code" href="classbdm_1_1eWishartCh.html#b745c73faef785009484180582050a1f" title="dimension of matrix ">p</a> );
619	<a name="l00886"></a>00886
620	<a name="l00887"></a>00887 <span class="comment">//sample diagonal</span>
621	<a name="l00888"></a>00888 <span class="keywordflow">for</span> ( <span class="keywordtype">int</span> i=0;i<<a class="code" href="classbdm_1_1eWishartCh.html#b745c73faef785009484180582050a1f" title="dimension of matrix ">p</a>;i++ )
622	<a name="l00889"></a>00889 {
623	<a name="l00890"></a>00890 GamRNG.<a class="code" href="classitpp_1_1Gamma__RNG.html#dfaae19411e39aa87e1f72e409b6babe" title="Set lambda.">setup</a> ( 0.5* ( <a class="code" href="classbdm_1_1eWishartCh.html#1879a14d7d2bb05062523b189baa11c3" title="degrees of freedom ">delta</a>-i ) , 0.5 ); <span class="comment">// no +1 !! index if from 0</span>
624	<a name="l00891"></a>00891 <span class="preprocessor">#pragma omp critical</span>
625	<a name="l00892"></a>00892 <span class="preprocessor"></span> X ( i,i ) =sqrt ( GamRNG() );
626	<a name="l00893"></a>00893 }
627	<a name="l00894"></a>00894 <span class="comment">//do the rest</span>
628	<a name="l00895"></a>00895 <span class="keywordflow">for</span> ( <span class="keywordtype">int</span> i=0;i<p;i++ )
629	<a name="l00896"></a>00896 {
630	<a name="l00897"></a>00897 <span class="keywordflow">for</span> ( <span class="keywordtype">int</span> j=i+1;j<p;j++ )
631	<a name="l00898"></a>00898 {
632	<a name="l00899"></a>00899 <span class="preprocessor">#pragma omp critical</span>
633	<a name="l00900"></a>00900 <span class="preprocessor"></span> X ( i,j ) =NorRNG.sample();
634	<a name="l00901"></a>00901 }
635	<a name="l00902"></a>00902 }
636	<a name="l00903"></a>00903 <span class="keywordflow">return</span> X*<a class="code" href="classbdm_1_1eWishartCh.html#1b42f9284a32f23b0b253a628cda7490" title="Upper-Triagle of Choleski decomposition of .">Y</a>.<a class="code" href="classchmat.html#9c50d31c999d85d8e9d8cf2b69b6ac8c" title="Access function.">_Ch</a>();<span class="comment">// return upper triangular part of the decomposition</span>
637	<a name="l00904"></a>00904 }
638	<a name="l00905"></a><a class="code" href="classbdm_1_1eWishartCh.html#8f2154b8b5be8f4c5788f261b6d57b9a">00905</a> vec <a class="code" href="classbdm_1_1eWishartCh.html#8f2154b8b5be8f4c5788f261b6d57b9a" title="Returns a sample, from density .">sample</a> ()<span class="keyword"> const</span>
639	<a name="l00906"></a>00906 <span class="keyword"> </span>{
640	<a name="l00907"></a>00907 <span class="keywordflow">return</span> vec ( sample_mat()._data(),p*p );
641	<a name="l00908"></a>00908 }
642	<a name="l00910"></a><a class="code" href="classbdm_1_1eWishartCh.html#4eee757c0535c2a88bb20f0767c64981">00910</a> <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1eWishartCh.html#4eee757c0535c2a88bb20f0767c64981" title="fast access function y0 will be copied into Y.Ch.">setY</a> ( <span class="keyword">const</span> mat &Ch0 ) {copy_vector ( <a class="code" href="classbdm_1_1epdf.html#16adac20ec7fe07e1ea0b27d917788ce" title="dimension of the random variable">dim</a>,Ch0._data(), <a class="code" href="classbdm_1_1eWishartCh.html#1b42f9284a32f23b0b253a628cda7490" title="Upper-Triagle of Choleski decomposition of .">Y</a>.<a class="code" href="classchmat.html#9c50d31c999d85d8e9d8cf2b69b6ac8c" title="Access function.">_Ch</a>()._data() );}
643	<a name="l00912"></a><a class="code" href="classbdm_1_1eWishartCh.html#7eac414ec10b85aa5536b0092c57bc4a">00912</a> <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1eWishartCh.html#7eac414ec10b85aa5536b0092c57bc4a" title="fast access function y0 will be copied into Y.Ch.">_setY</a> ( <span class="keyword">const</span> vec &ch0 ) {copy_vector ( <a class="code" href="classbdm_1_1epdf.html#16adac20ec7fe07e1ea0b27d917788ce" title="dimension of the random variable">dim</a>, ch0._data(), <a class="code" href="classbdm_1_1eWishartCh.html#1b42f9284a32f23b0b253a628cda7490" title="Upper-Triagle of Choleski decomposition of .">Y</a>.<a class="code" href="classchmat.html#9c50d31c999d85d8e9d8cf2b69b6ac8c" title="Access function.">_Ch</a>()._data() ); }
644	<a name="l00914"></a><a class="code" href="classbdm_1_1eWishartCh.html#1708cacb5d8cb1b96395d35f5327cb7e">00914</a> <span class="keyword">const</span> <a class="code" href="classchmat.html" title="Symmetric matrix stored in square root decomposition using upper cholesky.">chmat</a>& <a class="code" href="classbdm_1_1eWishartCh.html#1708cacb5d8cb1b96395d35f5327cb7e" title="access function">getY</a>()<span class="keyword">const </span>{<span class="keywordflow">return</span> <a class="code" href="classbdm_1_1eWishartCh.html#1b42f9284a32f23b0b253a628cda7490" title="Upper-Triagle of Choleski decomposition of .">Y</a>;}
645	<a name="l00915"></a>00915 };
646	<a name="l00916"></a>00916
647	<a name="l00917"></a>00917 <span class="keyword">class </span>eiWishartCh: <span class="keyword">public</span> epdf
648	<a name="l00918"></a>00918 {
649	<a name="l00919"></a>00919 <span class="keyword">protected</span>:
650	<a name="l00920"></a>00920 eWishartCh W;
651	<a name="l00921"></a>00921 <span class="keywordtype">int</span> p;
652	<a name="l00922"></a>00922 <span class="keywordtype">double</span> delta;
653	<a name="l00923"></a>00923 <span class="keyword">public</span>:
654	<a name="l00924"></a>00924 <span class="keywordtype">void</span> set_parameters ( <span class="keyword">const</span> mat &Y0, <span class="keyword">const</span> <span class="keywordtype">double</span> delta0) {
655	<a name="l00925"></a>00925 delta = delta0;
656	<a name="l00926"></a>00926 W.set_parameters ( inv ( Y0 ),delta0 );
657	<a name="l00927"></a>00927 dim = W.dimension(); p=Y0.rows();
658	<a name="l00928"></a>00928 }
659	<a name="l00929"></a>00929 vec sample()<span class="keyword"> const </span>{mat iCh; iCh=inv ( W.sample_mat() ); <span class="keywordflow">return</span> vec ( iCh._data(),<a class="code" href="classbdm_1_1epdf.html#16adac20ec7fe07e1ea0b27d917788ce" title="dimension of the random variable">dim</a> );}
660	<a name="l00930"></a>00930 <span class="keywordtype">void</span> _setY ( <span class="keyword">const</span> vec &y0 )
661	<a name="l00931"></a>00931 {
662	<a name="l00932"></a>00932 mat Ch ( p,p );
663	<a name="l00933"></a>00933 mat iCh ( p,p );
664	<a name="l00934"></a>00934 copy_vector ( dim, y0._data(), Ch._data() );
665	<a name="l00935"></a>00935
666	<a name="l00936"></a>00936 iCh=inv ( Ch );
667	<a name="l00937"></a>00937 W.setY ( iCh );
668	<a name="l00938"></a>00938 }
669	<a name="l00939"></a>00939 <span class="keyword">virtual</span> <span class="keywordtype">double</span> evallog ( <span class="keyword">const</span> vec &val )<span class="keyword"> const </span>{
670	<a name="l00940"></a>00940 <a class="code" href="classchmat.html" title="Symmetric matrix stored in square root decomposition using upper cholesky.">chmat</a> X(p);
671	<a name="l00941"></a>00941 <span class="keyword">const</span> <a class="code" href="classchmat.html" title="Symmetric matrix stored in square root decomposition using upper cholesky.">chmat</a>& Y=W.getY();
672	<a name="l00942"></a>00942
673	<a name="l00943"></a>00943 copy_vector(p*p,val._data(),X._Ch()._data());
674	<a name="l00944"></a>00944 <a class="code" href="classchmat.html" title="Symmetric matrix stored in square root decomposition using upper cholesky.">chmat</a> iX(p);X.inv(iX);
675	<a name="l00945"></a>00945 <span class="comment">// compute </span>
676	<a name="l00946"></a>00946 <span class="comment">// \frac{ \|\Psi\|^{m/2}\|X\|^{-(m+p+1)/2}e^{-tr(\Psi X^{-1})/2} }{ 2^{mp/2}\Gamma_p(m/2)},</span>
677	<a name="l00947"></a>00947 mat M=Y.<a class="code" href="classchmat.html#045addd685f8d978efda232d7dcb070e" title="Conversion to full matrix.">to_mat</a>()*iX.to_mat();
678	<a name="l00948"></a>00948
679	<a name="l00949"></a>00949 <span class="keywordtype">double</span> log1 = 0.5p(2Y.<a class="code" href="classchmat.html#b504ca818203b13e667cb3c503980382" title="Logarithm of a determinant.">logdet</a>())-0.5(delta+p+1)(2X.logdet())-0.5*trace(M);
680	<a name="l00950"></a>00950 <span class="comment">//Fixme! Multivariate gamma omitted!! it is ok for sampling, but not otherwise!!</span>
681	<a name="l00951"></a>00951
682	<a name="l00952"></a>00952 <span class="comment">/* if (0) {</span>
683	<a name="l00953"></a>00953 <span class="comment"> mat XX=X.to_mat();</span>
684	<a name="l00954"></a>00954 <span class="comment"> mat YY=Y.to_mat();</span>
685	<a name="l00955"></a>00955 <span class="comment"> </span>
686	<a name="l00956"></a>00956 <span class="comment"> double log2 = 0.5plog(det(YY))-0.5(delta+p+1)log(det(XX))-0.5trace(YYinv(XX)); </span>
687	<a name="l00957"></a>00957 <span class="comment"> cout << log1 << "," << log2 << endl;</span>
688	<a name="l00958"></a>00958 <span class="comment"> }*/</span>
689	<a name="l00959"></a>00959 <span class="keywordflow">return</span> log1;
690	<a name="l00960"></a>00960 };
691	<a name="l00961"></a>00961
692	<a name="l00962"></a>00962 };
693	<a name="l00963"></a>00963
694	<a name="l00964"></a>00964 <span class="keyword">class </span>rwiWishartCh : <span class="keyword">public</span> mpdf
695	<a name="l00965"></a>00965 {
696	<a name="l00966"></a>00966 <span class="keyword">protected</span>:
697	<a name="l00967"></a>00967 eiWishartCh eiW;
698	<a name="l00969"></a>00969 <span class="keywordtype">double</span> sqd;
699	<a name="l00970"></a>00970 <span class="comment">//reference point for diagonal</span>
700	<a name="l00971"></a>00971 vec refl;
701	<a name="l00972"></a>00972 <span class="keywordtype">double</span> l;
702	<a name="l00973"></a>00973 <span class="keywordtype">int</span> p;
703	<a name="l00974"></a>00974 <span class="keyword">public</span>:
704	<a name="l00975"></a>00975 <span class="keywordtype">void</span> set_parameters ( <span class="keywordtype">int</span> p0, <span class="keywordtype">double</span> k, vec ref0, <span class="keywordtype">double</span> l0 )
705	<a name="l00976"></a>00976 {
706	<a name="l00977"></a>00977 p=p0;
707	<a name="l00978"></a>00978 <span class="keywordtype">double</span> delta = 2/(k*k)+p+3;
708	<a name="l00979"></a>00979 sqd=sqrt ( delta-p-1 );
709	<a name="l00980"></a>00980 l=l0;
710	<a name="l00981"></a>00981 refl=pow(ref0,1-l);
711	<a name="l00982"></a>00982
712	<a name="l00983"></a>00983 eiW.set_parameters ( eye ( p ),delta );
713	<a name="l00984"></a>00984 <a class="code" href="classbdm_1_1mpdf.html#5eea43c56d38e4441bfb30270db949c0" title="pointer to internal epdf">ep</a>=&eiW;
714	<a name="l00985"></a>00985 <a class="code" href="classbdm_1_1mpdf.html#7c1900976ff13dbc09c9729b3bbff9e6" title="dimension of the condition">dimc</a>=eiW.dimension();
715	<a name="l00986"></a>00986 }
716	<a name="l00987"></a>00987 <span class="keywordtype">void</span> condition ( <span class="keyword">const</span> vec &c ) {
717	<a name="l00988"></a>00988 vec z=c;
718	<a name="l00989"></a>00989 <span class="keywordtype">int</span> ri=0;
719	<a name="l00990"></a>00990 <span class="keywordflow">for</span>(<span class="keywordtype">int</span> i=0;i<p*p;i+=(p+1)){<span class="comment">//trace diagonal element</span>
720	<a name="l00991"></a>00991 z(i) = pow(z(i),l)*refl(ri);
721	<a name="l00992"></a>00992 ri++;
722	<a name="l00993"></a>00993 }
723	<a name="l00994"></a>00994
724	<a name="l00995"></a>00995 eiW._setY ( sqd*z );
725	<a name="l00996"></a>00996 }
726	<a name="l00997"></a>00997 };
727	<a name="l00998"></a>00998
728	<a name="l01000"></a>01000 <span class="keyword">enum</span> RESAMPLING_METHOD { MULTINOMIAL = 0, STRATIFIED = 1, SYSTEMATIC = 3 };
729	<a name="l01006"></a><a class="code" href="classbdm_1_1eEmp.html">01006</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>
730	<a name="l01007"></a>01007 {
731	<a name="l01008"></a>01008 <span class="keyword">protected</span> :
732	<a name="l01010"></a><a class="code" href="classbdm_1_1eEmp.html#9798006271ca77629855113f1283a031">01010</a> <span class="keywordtype">int</span> <a class="code" href="classbdm_1_1eEmp.html#9798006271ca77629855113f1283a031" title="Number of particles.">n</a>;
733	<a name="l01012"></a><a class="code" href="classbdm_1_1eEmp.html#9d39241aab7c4bbeb07c6d516421c67d">01012</a> vec <a class="code" href="classbdm_1_1eEmp.html#9d39241aab7c4bbeb07c6d516421c67d" title="Sample weights .">w</a>;
734	<a name="l01014"></a><a class="code" href="classbdm_1_1eEmp.html#73d819553a0f268b055a087d2d4486f3">01014</a> Array<vec> <a class="code" href="classbdm_1_1eEmp.html#73d819553a0f268b055a087d2d4486f3" title="Samples .">samples</a>;
735	<a name="l01015"></a>01015 <span class="keyword">public</span>:
736	<a name="l01018"></a>01018 <a class="code" href="classbdm_1_1eEmp.html" title="Weighted empirical density.">eEmp</a> ( ) :<a class="code" href="classbdm_1_1epdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a> ( ),<a class="code" href="classbdm_1_1eEmp.html#9d39241aab7c4bbeb07c6d516421c67d" title="Sample weights .">w</a> ( ),<a class="code" href="classbdm_1_1eEmp.html#73d819553a0f268b055a087d2d4486f3" title="Samples .">samples</a> ( ) {};
737	<a name="l01019"></a>01019 <a class="code" href="classbdm_1_1eEmp.html" title="Weighted empirical density.">eEmp</a> ( <span class="keyword">const</span> <a class="code" href="classbdm_1_1eEmp.html" title="Weighted empirical density.">eEmp</a> &e ) : <a class="code" href="classbdm_1_1epdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a> ( e ), <a class="code" href="classbdm_1_1eEmp.html#9d39241aab7c4bbeb07c6d516421c67d" title="Sample weights .">w</a> ( e.<a class="code" href="classbdm_1_1eEmp.html#9d39241aab7c4bbeb07c6d516421c67d" title="Sample weights .">w</a> ), <a class="code" href="classbdm_1_1eEmp.html#73d819553a0f268b055a087d2d4486f3" title="Samples .">samples</a> ( e.<a class="code" href="classbdm_1_1eEmp.html#73d819553a0f268b055a087d2d4486f3" title="Samples .">samples</a> ) {};
738	<a name="l01021"></a>01021
739	<a name="l01023"></a>01023 <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1eEmp.html#7cfd383180b486fe4526bdf0179350c0" title="Set samples and weights.">set_statistics</a> ( <span class="keyword">const</span> vec &w0, <span class="keyword">const</span> epdf* pdf0 );
740	<a name="l01025"></a><a class="code" href="classbdm_1_1eEmp.html#cef74aa5f87d10d440b9b1e8bc78c1e7">01025</a> <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1eEmp.html#cef74aa5f87d10d440b9b1e8bc78c1e7" title="Set samples and weights.">set_statistics</a> ( <span class="keyword">const</span> <a class="code" href="classbdm_1_1epdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a>* pdf0 , <span class="keywordtype">int</span> <a class="code" href="classbdm_1_1eEmp.html#9798006271ca77629855113f1283a031" title="Number of particles.">n</a> ) {<a class="code" href="classbdm_1_1eEmp.html#cef74aa5f87d10d440b9b1e8bc78c1e7" title="Set samples and weights.">set_statistics</a> ( ones ( n ) /n,pdf0 );};
741	<a name="l01027"></a>01027 <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 );
742	<a name="l01029"></a><a class="code" href="classbdm_1_1eEmp.html#c74c281d652356c19b6b079e42ca7ef1">01029</a> <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1eEmp.html#c74c281d652356c19b6b079e42ca7ef1" title="Set sample.">set_parameters</a> ( <span class="keywordtype">int</span> n0, <span class="keywordtype">bool</span> copy=<span class="keyword">true</span> ) {<a class="code" href="classbdm_1_1eEmp.html#9798006271ca77629855113f1283a031" title="Number of particles.">n</a>=n0; <a class="code" href="classbdm_1_1eEmp.html#9d39241aab7c4bbeb07c6d516421c67d" title="Sample weights .">w</a>.set_size ( n0,copy );<a class="code" href="classbdm_1_1eEmp.html#73d819553a0f268b055a087d2d4486f3" title="Samples .">samples</a>.set_size ( n0,copy );};
743	<a name="l01031"></a><a class="code" href="classbdm_1_1eEmp.html#d7f83cc0415cd44ae7cc8b4bdad93aef">01031</a> vec& <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>;};
744	<a name="l01033"></a><a class="code" href="classbdm_1_1eEmp.html#b7d7106f486e3fad38590914a693d714">01033</a> <span class="keyword">const</span> vec& <a class="code" href="classbdm_1_1eEmp.html#b7d7106f486e3fad38590914a693d714" title="Potentially dangerous, use with care.">_w</a>()<span class="keyword"> const </span>{<span class="keywordflow">return</span> <a class="code" href="classbdm_1_1eEmp.html#9d39241aab7c4bbeb07c6d516421c67d" title="Sample weights .">w</a>;};
745	<a name="l01035"></a><a class="code" href="classbdm_1_1eEmp.html#c24966b0aaeb767bc8a6b4fd60931be2">01035</a> Array<vec>& <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>;};
746	<a name="l01037"></a><a class="code" href="classbdm_1_1eEmp.html#b59af0efdb009d98ea8ebfa965e74ae2">01037</a> <span class="keyword">const</span> Array<vec>& <a class="code" href="classbdm_1_1eEmp.html#b59af0efdb009d98ea8ebfa965e74ae2" title="access function">_samples</a>()<span class="keyword"> const </span>{<span class="keywordflow">return</span> <a class="code" href="classbdm_1_1eEmp.html#73d819553a0f268b055a087d2d4486f3" title="Samples .">samples</a>;};
747	<a name="l01039"></a>01039 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 );
748	<a name="l01041"></a><a class="code" href="classbdm_1_1eEmp.html#97f1e07b5ae6eebc91c7365f0f88d270">01041</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;}
749	<a name="l01043"></a><a class="code" href="classbdm_1_1eEmp.html#01654c014d3aa068f8d4ecba4be86d09">01043</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 &val )<span class="keyword"> const </span>{it_error ( <span class="stringliteral">"Not implemented"</span> );<span class="keywordflow">return</span> 0.0;}
750	<a name="l01044"></a><a class="code" href="classbdm_1_1eEmp.html#bbfcb4f868c7381298c281a256d8c4b9">01044</a> vec <a class="code" href="classbdm_1_1eEmp.html#bbfcb4f868c7381298c281a256d8c4b9" title="return expected value">mean</a>()<span class="keyword"> const</span>
751	<a name="l01045"></a>01045 <span class="keyword"> </span>{
752	<a name="l01046"></a>01046 vec pom=zeros ( <a class="code" href="classbdm_1_1epdf.html#16adac20ec7fe07e1ea0b27d917788ce" title="dimension of the random variable">dim</a> );
753	<a name="l01047"></a>01047 <span class="keywordflow">for</span> ( <span class="keywordtype">int</span> i=0;i<<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 );}
754	<a name="l01048"></a>01048 <span class="keywordflow">return</span> pom;
755	<a name="l01049"></a>01049 }
756	<a name="l01050"></a><a class="code" href="classbdm_1_1eEmp.html#05e9ebf467ede737cb6a3621d7fd3c87">01050</a> vec <a class="code" href="classbdm_1_1eEmp.html#05e9ebf467ede737cb6a3621d7fd3c87" title="return expected variance (not covariance!)">variance</a>()<span class="keyword"> const</span>
757	<a name="l01051"></a>01051 <span class="keyword"> </span>{
758	<a name="l01052"></a>01052 vec pom=zeros ( <a class="code" href="classbdm_1_1epdf.html#16adac20ec7fe07e1ea0b27d917788ce" title="dimension of the random variable">dim</a> );
759	<a name="l01053"></a>01053 <span class="keywordflow">for</span> ( <span class="keywordtype">int</span> i=0;i<<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 );}
760	<a name="l01054"></a>01054 <span class="keywordflow">return</span> pom-pow ( <a class="code" href="classbdm_1_1eEmp.html#bbfcb4f868c7381298c281a256d8c4b9" title="return expected value">mean</a>(),2 );
761	<a name="l01055"></a>01055 }
762	<a name="l01057"></a><a class="code" href="classbdm_1_1eEmp.html#b1c9df656144edf79ba2d885613f661f">01057</a> <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1eEmp.html#b1c9df656144edf79ba2d885613f661f" title="For this class, qbounds are minimum and maximum value of the population!">qbounds</a> ( vec &lb, vec &ub, <span class="keywordtype">double</span> perc=0.95 )<span class="keyword"> const</span>
763	<a name="l01058"></a>01058 <span class="keyword"> </span>{
764	<a name="l01059"></a>01059 <span class="comment">// lb in inf so than it will be pushed below;</span>
765	<a name="l01060"></a>01060 lb.set_size ( <a class="code" href="classbdm_1_1epdf.html#16adac20ec7fe07e1ea0b27d917788ce" title="dimension of the random variable">dim</a> );
766	<a name="l01061"></a>01061 ub.set_size ( <a class="code" href="classbdm_1_1epdf.html#16adac20ec7fe07e1ea0b27d917788ce" title="dimension of the random variable">dim</a> );
767	<a name="l01062"></a>01062 lb = std::numeric_limits<double>::infinity();
768	<a name="l01063"></a>01063 ub = -std::numeric_limits<double>::infinity();
769	<a name="l01064"></a>01064 <span class="keywordtype">int</span> j;
770	<a name="l01065"></a>01065 <span class="keywordflow">for</span> ( <span class="keywordtype">int</span> i=0;i<<a class="code" href="classbdm_1_1eEmp.html#9798006271ca77629855113f1283a031" title="Number of particles.">n</a>;i++ )
771	<a name="l01066"></a>01066 {
772	<a name="l01067"></a>01067 <span class="keywordflow">for</span> ( j=0;j<<a class="code" href="classbdm_1_1epdf.html#16adac20ec7fe07e1ea0b27d917788ce" title="dimension of the random variable">dim</a>; j++ )
773	<a name="l01068"></a>01068 {
774	<a name="l01069"></a>01069 <span class="keywordflow">if</span> ( <a class="code" href="classbdm_1_1eEmp.html#73d819553a0f268b055a087d2d4486f3" title="Samples .">samples</a> ( i ) ( j ) <lb ( j ) ) {lb ( j ) =<a class="code" href="classbdm_1_1eEmp.html#73d819553a0f268b055a087d2d4486f3" title="Samples .">samples</a> ( i ) ( j );}
775	<a name="l01070"></a>01070 <span class="keywordflow">if</span> ( <a class="code" href="classbdm_1_1eEmp.html#73d819553a0f268b055a087d2d4486f3" title="Samples .">samples</a> ( i ) ( j ) >ub ( j ) ) {ub ( j ) =<a class="code" href="classbdm_1_1eEmp.html#73d819553a0f268b055a087d2d4486f3" title="Samples .">samples</a> ( i ) ( j );}
776	<a name="l01071"></a>01071 }
777	<a name="l01072"></a>01072 }
778	<a name="l01073"></a>01073 }
779	<a name="l01074"></a>01074 };
780	<a name="l01075"></a>01075
781	<a name="l01076"></a>01076
782	<a name="l01078"></a>01078
783	<a name="l01079"></a>01079 <span class="keyword">template</span><<span class="keyword">class</span> sq_T>
784	<a name="l01080"></a>01080 <span class="keywordtype">void</span> enorm<sq_T>::set_parameters ( <span class="keyword">const</span> vec &mu0, <span class="keyword">const</span> sq_T &R0 )
785	<a name="l01081"></a>01081 {
786	<a name="l01082"></a>01082 <span class="comment">//Fixme test dimensions of mu0 and R0;</span>
787	<a name="l01083"></a>01083 mu = mu0;
788	<a name="l01084"></a>01084 R = R0;
789	<a name="l01085"></a>01085 <a class="code" href="classbdm_1_1epdf.html#16adac20ec7fe07e1ea0b27d917788ce" title="dimension of the random variable">dim</a> = mu0.length();
790	<a name="l01086"></a>01086 };
791	<a name="l01087"></a>01087
792	<a name="l01088"></a>01088 <span class="keyword">template</span><<span class="keyword">class</span> sq_T>
793	<a name="l01089"></a><a class="code" href="classbdm_1_1enorm.html#d2e0d3a1e30ab3ab04df2d0c43ae74a2">01089</a> <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1enorm.html#d2e0d3a1e30ab3ab04df2d0c43ae74a2" title="dupdate in exponential form (not really handy)">enorm<sq_T>::dupdate</a> ( mat &v, <span class="keywordtype">double</span> nu )
794	<a name="l01090"></a>01090 {
795	<a name="l01091"></a>01091 <span class="comment">//</span>
796	<a name="l01092"></a>01092 };
797	<a name="l01093"></a>01093
798	<a name="l01094"></a>01094 <span class="comment">// template<class sq_T></span>
799	<a name="l01095"></a>01095 <span class="comment">// void enorm<sq_T>::tupdate ( double phi, mat &vbar, double nubar ) {</span>
800	<a name="l01096"></a>01096 <span class="comment">// //</span>
801	<a name="l01097"></a>01097 <span class="comment">// };</span>
802	<a name="l01098"></a>01098
803	<a name="l01099"></a>01099 <span class="keyword">template</span><<span class="keyword">class</span> sq_T>
804	<a name="l01100"></a><a class="code" href="classbdm_1_1enorm.html#e1a48f52351ec3a349bd443b713b1766">01100</a> vec <a class="code" href="classbdm_1_1enorm.html#e1a48f52351ec3a349bd443b713b1766" title="Returns a sample, from density .">enorm<sq_T>::sample</a>()<span class="keyword"> const</span>
805	<a name="l01101"></a>01101 <span class="keyword"> </span>{
806	<a name="l01102"></a>01102 vec x ( <a class="code" href="classbdm_1_1epdf.html#16adac20ec7fe07e1ea0b27d917788ce" title="dimension of the random variable">dim</a> );
807	<a name="l01103"></a>01103 <span class="preprocessor">#pragma omp critical</span>
808	<a name="l01104"></a>01104 <span class="preprocessor"></span> NorRNG.sample_vector ( <a class="code" href="classbdm_1_1epdf.html#16adac20ec7fe07e1ea0b27d917788ce" title="dimension of the random variable">dim</a>,x );
809	<a name="l01105"></a>01105 vec smp = <a class="code" href="classbdm_1_1enorm.html#2d92dde696b2a7a5b10ddef5d22ba2c2" title="Covariance matrix in decomposed form.">R</a>.sqrt_mult ( x );
810	<a name="l01106"></a>01106
811	<a name="l01107"></a>01107 smp += <a class="code" href="classbdm_1_1enorm.html#c702a194720853570d08b65482f842c7" title="mean value">mu</a>;
812	<a name="l01108"></a>01108 <span class="keywordflow">return</span> smp;
813	<a name="l01109"></a>01109 };
814	<a name="l01110"></a>01110
815	<a name="l01111"></a>01111 <span class="keyword">template</span><<span class="keyword">class</span> sq_T>
816	<a name="l01112"></a>01112 mat <a class="code" href="classbdm_1_1enorm.html#e1a48f52351ec3a349bd443b713b1766" title="Returns a sample, from density .">enorm<sq_T>::sample</a> ( <span class="keywordtype">int</span> N )<span class="keyword"> const</span>
817	<a name="l01113"></a>01113 <span class="keyword"> </span>{
818	<a name="l01114"></a>01114 mat X ( <a class="code" href="classbdm_1_1epdf.html#16adac20ec7fe07e1ea0b27d917788ce" title="dimension of the random variable">dim</a>,N );
819	<a name="l01115"></a>01115 vec x ( <a class="code" href="classbdm_1_1epdf.html#16adac20ec7fe07e1ea0b27d917788ce" title="dimension of the random variable">dim</a> );
820	<a name="l01116"></a>01116 vec pom;
821	<a name="l01117"></a>01117 <span class="keywordtype">int</span> i;
822	<a name="l01118"></a>01118
823	<a name="l01119"></a>01119 <span class="keywordflow">for</span> ( i=0;i<N;i++ )
824	<a name="l01120"></a>01120 {
825	<a name="l01121"></a>01121 <span class="preprocessor">#pragma omp critical</span>
826	<a name="l01122"></a>01122 <span class="preprocessor"></span> NorRNG.sample_vector ( <a class="code" href="classbdm_1_1epdf.html#16adac20ec7fe07e1ea0b27d917788ce" title="dimension of the random variable">dim</a>,x );
827	<a name="l01123"></a>01123 pom = <a class="code" href="classbdm_1_1enorm.html#2d92dde696b2a7a5b10ddef5d22ba2c2" title="Covariance matrix in decomposed form.">R</a>.sqrt_mult ( x );
828	<a name="l01124"></a>01124 pom +=<a class="code" href="classbdm_1_1enorm.html#c702a194720853570d08b65482f842c7" title="mean value">mu</a>;
829	<a name="l01125"></a>01125 X.set_col ( i, pom );
830	<a name="l01126"></a>01126 }
831	<a name="l01127"></a>01127
832	<a name="l01128"></a>01128 <span class="keywordflow">return</span> X;
833	<a name="l01129"></a>01129 };
834	<a name="l01130"></a>01130
835	<a name="l01131"></a>01131 <span class="comment">// template<class sq_T></span>
836	<a name="l01132"></a>01132 <span class="comment">// double enorm<sq_T>::eval ( const vec &val ) const {</span>
837	<a name="l01133"></a>01133 <span class="comment">// double pdfl,e;</span>
838	<a name="l01134"></a>01134 <span class="comment">// pdfl = evallog ( val );</span>
839	<a name="l01135"></a>01135 <span class="comment">// e = exp ( pdfl );</span>
840	<a name="l01136"></a>01136 <span class="comment">// return e;</span>
841	<a name="l01137"></a>01137 <span class="comment">// };</span>
842	<a name="l01138"></a>01138
843	<a name="l01139"></a>01139 <span class="keyword">template</span><<span class="keyword">class</span> sq_T>
844	<a name="l01140"></a><a class="code" href="classbdm_1_1enorm.html#e13aeed5b543b2179bacdc4fa2ae47a3">01140</a> <span class="keywordtype">double</span> <a class="code" href="classbdm_1_1enorm.html#e13aeed5b543b2179bacdc4fa2ae47a3" title="Evaluate normalized log-probability.">enorm<sq_T>::evallog_nn</a> ( <span class="keyword">const</span> vec &val )<span class="keyword"> const</span>
845	<a name="l01141"></a>01141 <span class="keyword"> </span>{
846	<a name="l01142"></a>01142 <span class="comment">// 1.83787706640935 = log(2pi)</span>
847	<a name="l01143"></a>01143 <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>
848	<a name="l01144"></a>01144 <span class="keywordflow">return</span> tmp;
849	<a name="l01145"></a>01145 };
850	<a name="l01146"></a>01146
851	<a name="l01147"></a>01147 <span class="keyword">template</span><<span class="keyword">class</span> sq_T>
852	<a name="l01148"></a><a class="code" href="classbdm_1_1enorm.html#25785343aff102cc5df1cab08ba16d32">01148</a> <span class="keyword">inline</span> <span class="keywordtype">double</span> <a class="code" href="classbdm_1_1enorm.html#25785343aff102cc5df1cab08ba16d32" title="logarithm of the normalizing constant, ">enorm<sq_T>::lognc</a> ()<span class="keyword"> const</span>
853	<a name="l01149"></a>01149 <span class="keyword"> </span>{
854	<a name="l01150"></a>01150 <span class="comment">// 1.83787706640935 = log(2pi)</span>
855	<a name="l01151"></a>01151 <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() );
856	<a name="l01152"></a>01152 <span class="keywordflow">return</span> tmp;
857	<a name="l01153"></a>01153 };
858	<a name="l01154"></a>01154
859	<a name="l01155"></a>01155 <span class="keyword">template</span><<span class="keyword">class</span> sq_T>
860	<a name="l01156"></a><a class="code" href="classbdm_1_1mlnorm.html#5d18dec3167584338a4775c1d165d96f">01156</a> <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1mlnorm.html#5d18dec3167584338a4775c1d165d96f" title="Set A and R.">mlnorm<sq_T>::set_parameters</a> ( <span class="keyword">const</span> mat &A0, <span class="keyword">const</span> vec &mu0, <span class="keyword">const</span> sq_T &R0 )
861	<a name="l01157"></a>01157 {
862	<a name="l01158"></a>01158 it_assert_debug ( A0.rows() ==mu0.length(),<span class="stringliteral">""</span> );
863	<a name="l01159"></a>01159 it_assert_debug ( A0.rows() ==R0.rows(),<span class="stringliteral">""</span> );
864	<a name="l01160"></a>01160
865	<a name="l01161"></a>01161 <a class="code" href="classbdm_1_1epdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a>.set_parameters ( zeros ( A0.rows() ),R0 );
866	<a name="l01162"></a>01162 A = A0;
867	<a name="l01163"></a>01163 mu_const = mu0;
868	<a name="l01164"></a>01164 <a class="code" href="classbdm_1_1mpdf.html#7c1900976ff13dbc09c9729b3bbff9e6" title="dimension of the condition">dimc</a>=A0.cols();
869	<a name="l01165"></a>01165 }
870	<a name="l01166"></a>01166
871	<a name="l01167"></a>01167 <span class="comment">// template<class sq_T></span>
872	<a name="l01168"></a>01168 <span class="comment">// vec mlnorm<sq_T>::samplecond (const vec &cond, double &lik ) {</span>
873	<a name="l01169"></a>01169 <span class="comment">// this->condition ( cond );</span>
874	<a name="l01170"></a>01170 <span class="comment">// vec smp = epdf.sample();</span>
875	<a name="l01171"></a>01171 <span class="comment">// lik = epdf.eval ( smp );</span>
876	<a name="l01172"></a>01172 <span class="comment">// return smp;</span>
877	<a name="l01173"></a>01173 <span class="comment">// }</span>
878	<a name="l01174"></a>01174
879	<a name="l01175"></a>01175 <span class="comment">// template<class sq_T></span>
880	<a name="l01176"></a>01176 <span class="comment">// mat mlnorm<sq_T>::samplecond (const vec &cond, vec &lik, int n ) {</span>
881	<a name="l01177"></a>01177 <span class="comment">// int i;</span>
882	<a name="l01178"></a>01178 <span class="comment">// int dim = rv.count();</span>
883	<a name="l01179"></a>01179 <span class="comment">// mat Smp ( dim,n );</span>
884	<a name="l01180"></a>01180 <span class="comment">// vec smp ( dim );</span>
885	<a name="l01181"></a>01181 <span class="comment">// this->condition ( cond );</span>
886	<a name="l01182"></a>01182 <span class="comment">//</span>
887	<a name="l01183"></a>01183 <span class="comment">// for ( i=0; i<n; i++ ) {</span>
888	<a name="l01184"></a>01184 <span class="comment">// smp = epdf.sample();</span>
889	<a name="l01185"></a>01185 <span class="comment">// lik ( i ) = epdf.eval ( smp );</span>
890	<a name="l01186"></a>01186 <span class="comment">// Smp.set_col ( i ,smp );</span>
891	<a name="l01187"></a>01187 <span class="comment">// }</span>
892	<a name="l01188"></a>01188 <span class="comment">//</span>
893	<a name="l01189"></a>01189 <span class="comment">// return Smp;</span>
894	<a name="l01190"></a>01190 <span class="comment">// }</span>
895	<a name="l01191"></a>01191
896	<a name="l01192"></a>01192 <span class="keyword">template</span><<span class="keyword">class</span> sq_T>
897	<a name="l01193"></a><a class="code" href="classbdm_1_1mlnorm.html#0dafc0196e7e07fd06dc6716e0e18bc2">01193</a> <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1mlnorm.html#0dafc0196e7e07fd06dc6716e0e18bc2">mlnorm<sq_T>::condition</a> ( <span class="keyword">const</span> vec &cond )
898	<a name="l01194"></a>01194 {
899	<a name="l01195"></a>01195 _mu = A*cond + mu_const;
900	<a name="l01196"></a>01196 <span class="comment">//R is already assigned;</span>
901	<a name="l01197"></a>01197 }
902	<a name="l01198"></a>01198
903	<a name="l01199"></a>01199 <span class="keyword">template</span><<span class="keyword">class</span> sq_T>
904	<a name="l01200"></a><a class="code" href="classbdm_1_1enorm.html#c2996bdaffad38fbe0fc776db54c9d80">01200</a> <a class="code" href="classbdm_1_1enorm.html" title="Gaussian density with positive definite (decomposed) covariance matrix.">enorm<sq_T></a>* <a class="code" href="classbdm_1_1enorm.html#c2996bdaffad38fbe0fc776db54c9d80" title="Return marginal density on the given RV, the remainig rvs are intergrated out.">enorm<sq_T>::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> &rvn )<span class="keyword"> const</span>
905	<a name="l01201"></a>01201 <span class="keyword"> </span>{
906	<a name="l01202"></a>01202 it_assert_debug ( <a class="code" href="classbdm_1_1epdf.html#c4b863ff84c7a4882fb3ad18556027f9" title="True if rv is assigned.">isnamed</a>(), <span class="stringliteral">"rv description is not assigned"</span> );
907	<a name="l01203"></a>01203 ivec irvn = rvn.<a class="code" href="classbdm_1_1RV.html#cbebdb5e0d30101a6eb63550ef701c55">dataind</a> ( <a class="code" href="classbdm_1_1epdf.html#62c5b8ff71d9ebe6cd58d3c342eb1dc8" title="Description of the random variable.">rv</a> );
908	<a name="l01204"></a>01204
909	<a name="l01205"></a>01205 sq_T Rn ( <a class="code" href="classbdm_1_1enorm.html#2d92dde696b2a7a5b10ddef5d22ba2c2" title="Covariance matrix in decomposed form.">R</a>,irvn ); <span class="comment">//select rows and columns of R</span>
910	<a name="l01206"></a>01206
911	<a name="l01207"></a>01207 <a class="code" href="classbdm_1_1enorm.html" title="Gaussian density with positive definite (decomposed) covariance matrix.">enorm<sq_T></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<sq_T></a>;
912	<a name="l01208"></a>01208 tmp-><a class="code" href="classbdm_1_1epdf.html#f423e28448dbb69ef4905295ec8de8ff" title="Name its rv.">set_rv</a> ( rvn );
913	<a name="l01209"></a>01209 tmp-><a class="code" href="classbdm_1_1enorm.html#b8322f2c11560871dd922c660f4771bb">set_parameters</a> ( <a class="code" href="classbdm_1_1enorm.html#c702a194720853570d08b65482f842c7" title="mean value">mu</a> ( irvn ), Rn );
914	<a name="l01210"></a>01210 <span class="keywordflow">return</span> tmp;
915	<a name="l01211"></a>01211 }
916	<a name="l01212"></a>01212
917	<a name="l01213"></a>01213 <span class="keyword">template</span><<span class="keyword">class</span> sq_T>
918	<a name="l01214"></a><a class="code" href="classbdm_1_1enorm.html#baea4d49c657342b58297d68cda16d26">01214</a> <a class="code" href="classbdm_1_1mpdf.html" title="Conditional probability density, e.g. modeling some dependencies.">mpdf</a>* <a class="code" href="classbdm_1_1enorm.html#baea4d49c657342b58297d68cda16d26" title="Return conditional density on the given RV, the remaining rvs will be in conditioning...">enorm<sq_T>::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> &rvn )<span class="keyword"> const</span>
919	<a name="l01215"></a>01215 <span class="keyword"> </span>{
920	<a name="l01216"></a>01216
921	<a name="l01217"></a>01217 it_assert_debug ( <a class="code" href="classbdm_1_1epdf.html#c4b863ff84c7a4882fb3ad18556027f9" title="True if rv is assigned.">isnamed</a>(),<span class="stringliteral">"rvs are not assigned"</span> );
922	<a name="l01218"></a>01218
923	<a name="l01219"></a>01219 <a class="code" href="classbdm_1_1RV.html" title="Class representing variables, most often random variables.">RV</a> rvc = <a class="code" href="classbdm_1_1epdf.html#62c5b8ff71d9ebe6cd58d3c342eb1dc8" title="Description of the random variable.">rv</a>.<a class="code" href="classbdm_1_1RV.html#aec44dabdf0a6d90fbae95e1356eda39" title="Subtract another variable from the current one.">subt</a> ( rvn );
924	<a name="l01220"></a>01220 it_assert_debug ( ( rvc.<a class="code" href="classbdm_1_1RV.html#de30156104f61d86c94f758861418089">_dsize</a>() +rvn.<a class="code" href="classbdm_1_1RV.html#de30156104f61d86c94f758861418089">_dsize</a>() ==<a class="code" href="classbdm_1_1epdf.html#62c5b8ff71d9ebe6cd58d3c342eb1dc8" title="Description of the random variable.">rv</a>.<a class="code" href="classbdm_1_1RV.html#de30156104f61d86c94f758861418089">_dsize</a>() ),<span class="stringliteral">"wrong rvn"</span> );
925	<a name="l01221"></a>01221 <span class="comment">//Permutation vector of the new R</span>
926	<a name="l01222"></a>01222 ivec irvn = rvn.<a class="code" href="classbdm_1_1RV.html#cbebdb5e0d30101a6eb63550ef701c55">dataind</a> ( <a class="code" href="classbdm_1_1epdf.html#62c5b8ff71d9ebe6cd58d3c342eb1dc8" title="Description of the random variable.">rv</a> );
927	<a name="l01223"></a>01223 ivec irvc = rvc.<a class="code" href="classbdm_1_1RV.html#cbebdb5e0d30101a6eb63550ef701c55">dataind</a> ( <a class="code" href="classbdm_1_1epdf.html#62c5b8ff71d9ebe6cd58d3c342eb1dc8" title="Description of the random variable.">rv</a> );
928	<a name="l01224"></a>01224 ivec perm=concat ( irvn , irvc );
929	<a name="l01225"></a>01225 sq_T Rn ( <a class="code" href="classbdm_1_1enorm.html#2d92dde696b2a7a5b10ddef5d22ba2c2" title="Covariance matrix in decomposed form.">R</a>,perm );
930	<a name="l01226"></a>01226
931	<a name="l01227"></a>01227 <span class="comment">//fixme - could this be done in general for all sq_T?</span>
932	<a name="l01228"></a>01228 mat S=Rn.to_mat();
933	<a name="l01229"></a>01229 <span class="comment">//fixme</span>
934	<a name="l01230"></a>01230 <span class="keywordtype">int</span> n=rvn.<a class="code" href="classbdm_1_1RV.html#de30156104f61d86c94f758861418089">_dsize</a>()-1;
935	<a name="l01231"></a>01231 <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;
936	<a name="l01232"></a>01232 mat S11 = S.get ( 0,n, 0, n );
937	<a name="l01233"></a>01233 mat S12 = S.get ( 0, n , rvn.<a class="code" href="classbdm_1_1RV.html#de30156104f61d86c94f758861418089">_dsize</a>(), end );
938	<a name="l01234"></a>01234 mat S22 = S.get ( rvn.<a class="code" href="classbdm_1_1RV.html#de30156104f61d86c94f758861418089">_dsize</a>(), end, rvn.<a class="code" href="classbdm_1_1RV.html#de30156104f61d86c94f758861418089">_dsize</a>(), end );
939	<a name="l01235"></a>01235
940	<a name="l01236"></a>01236 vec mu1 = <a class="code" href="classbdm_1_1enorm.html#c702a194720853570d08b65482f842c7" title="mean value">mu</a> ( irvn );
941	<a name="l01237"></a>01237 vec mu2 = <a class="code" href="classbdm_1_1enorm.html#c702a194720853570d08b65482f842c7" title="mean value">mu</a> ( irvc );
942	<a name="l01238"></a>01238 mat A=S12*inv ( S22 );
943	<a name="l01239"></a>01239 sq_T R_n ( S11 - A *S12.T() );
944	<a name="l01240"></a>01240
945	<a name="l01241"></a>01241 <a class="code" href="classbdm_1_1mlnorm.html" title="Normal distributed linear function with linear function of mean value;.">mlnorm<sq_T></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<sq_T></a> ( );
946	<a name="l01242"></a>01242 tmp->set_rv ( rvn ); tmp->set_rvc ( rvc );
947	<a name="l01243"></a>01243 tmp->set_parameters ( A,mu1-A*mu2,R_n );
948	<a name="l01244"></a>01244 <span class="keywordflow">return</span> tmp;
949	<a name="l01245"></a>01245 }
950	<a name="l01246"></a>01246
951	<a name="l01248"></a>01248
952	<a name="l01249"></a>01249 <span class="keyword">template</span><<span class="keyword">class</span> sq_T>
953	<a name="l01250"></a>01250 std::ostream &operator<< ( std::ostream &os, mlnorm<sq_T> &ml )
954	<a name="l01251"></a>01251 {
955	<a name="l01252"></a>01252 os << <span class="stringliteral">"A:"</span><< ml.A<<endl;
956	<a name="l01253"></a>01253 os << <span class="stringliteral">"mu:"</span><< ml.mu_const<<endl;
957	<a name="l01254"></a>01254 os << <span class="stringliteral">"R:"</span> << ml.epdf._R().to_mat() <<endl;
958	<a name="l01255"></a>01255 <span class="keywordflow">return</span> os;
959	<a name="l01256"></a>01256 };
960	<a name="l01257"></a>01257
961	<a name="l01258"></a>01258 }
962	<a name="l01259"></a>01259 <span class="preprocessor">#endif //EF_H</span>
963	</pre></div></div>
964	<hr size="1"><address style="text-align: right;"><small>Generated on Mon Jun 8 18:02:33 2009 for mixpp by
965	<a href="http://www.doxygen.org/index.html">
966	<img src="doxygen.png" alt="doxygen" align="middle" border="0"></a> 1.5.8 </small></address>
967	</body>
968	</html>

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