Changeset 33 for doc/html/libEF_8h-source.html

Timestamp:

03/05/08 16:01:56 (16 years ago)

Author:

smidl

Message:

Oprava PF a MPF + jejich implementace pro pmsm system

Files:

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

doc/html/libEF_8h-source.html

@@ -28 +28 @@
 <a name="l00024"></a>00024 
 <a name="l00026"></a>00026 <span class="keyword">extern</span> Uniform_RNG UniRNG;
-<a name="l00027"></a>00027 <span class="keyword">extern</span> Normal_RNG NorRNG;
-<a name="l00028"></a>00028 <span class="keyword">extern</span> <a class="code" href="classitpp_1_1Gamma__RNG.html" title="Gamma distribution.">Gamma_RNG</a> GamRNG;
-<a name="l00029"></a>00029 
-<a name="l00030"></a>00030 
-<a name="l00037"></a><a class="code" href="classeEF.html">00037</a> <span class="keyword">class </span><a class="code" href="classeEF.html" title="General conjugate exponential family posterior density.">eEF</a> : <span class="keyword">public</span> <a class="code" href="classepdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a> {
-<a name="l00038"></a>00038 
-<a name="l00039"></a>00039 <span class="keyword">public</span>:
-<a name="l00041"></a><a class="code" href="classeEF.html#702e24158366430bc24d57c7f64e1e9e">00041</a>         <a class="code" href="classeEF.html#702e24158366430bc24d57c7f64e1e9e" title="default constructor">eEF</a>() :<a class="code" href="classepdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a>() {};
-<a name="l00042"></a>00042 
-<a name="l00043"></a>00043         <a class="code" href="classeEF.html#702e24158366430bc24d57c7f64e1e9e" title="default constructor">eEF</a> ( <span class="keyword">const</span> <a class="code" href="classRV.html" title="Class representing variables, most often random variables.">RV</a> &amp;rv ) :<a class="code" href="classepdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a> ( rv ) {};
-<a name="l00044"></a>00044 
-<a name="l00045"></a>00045         <span class="keyword">virtual</span> <span class="keywordtype">void</span> tupdate ( <span class="keywordtype">double</span> phi, mat &amp;vbar, <span class="keywordtype">double</span> nubar ) {};
-<a name="l00046"></a>00046 
-<a name="l00047"></a>00047         <span class="keyword">virtual</span> <span class="keywordtype">void</span> dupdate ( mat &amp;v,<span class="keywordtype">double</span> nu=1.0 ) {};
+<a name="l00028"></a>00028 <span class="keyword">extern</span> Normal_RNG NorRNG;
+<a name="l00030"></a>00030 <span class="keyword">extern</span> <a class="code" href="classitpp_1_1Gamma__RNG.html" title="Gamma distribution.">Gamma_RNG</a> GamRNG;
+<a name="l00031"></a>00031 
+<a name="l00038"></a><a class="code" href="classeEF.html">00038</a> <span class="keyword">class </span><a class="code" href="classeEF.html" title="General conjugate exponential family posterior density.">eEF</a> : <span class="keyword">public</span> <a class="code" href="classepdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a> {
+<a name="l00039"></a>00039 
+<a name="l00040"></a>00040 <span class="keyword">public</span>:
+<a name="l00041"></a>00041 <span class="comment">//      eEF() :epdf() {};</span>
+<a name="l00043"></a><a class="code" href="classeEF.html#7e3c63655e8375c76bf1f421245427a7">00043</a> <span class="comment"></span>        <a class="code" href="classeEF.html#7e3c63655e8375c76bf1f421245427a7" title="default constructor">eEF</a> ( <span class="keyword">const</span> <a class="code" href="classRV.html" title="Class representing variables, most often random variables.">RV</a> &amp;<a class="code" href="classepdf.html#74da992e3f5d598da8850b646b79b9d9" title="Identified of the random variable.">rv</a> ) :<a class="code" href="classepdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a> ( rv ) {};
+<a name="l00045"></a><a class="code" href="classeEF.html#fd88bc35550ec8fe9281d358216d0fcf">00045</a>         <span class="keyword">virtual</span> <span class="keywordtype">void</span> <a class="code" href="classeEF.html#fd88bc35550ec8fe9281d358216d0fcf" title="TODO decide if it is really needed.">tupdate</a> ( <span class="keywordtype">double</span> phi, mat &amp;vbar, <span class="keywordtype">double</span> nubar ) {};
+<a name="l00047"></a><a class="code" href="classeEF.html#5863718c3b2fb1496dece10c5b745d5c">00047</a>         <span class="keyword">virtual</span> <span class="keywordtype">void</span> <a class="code" href="classeEF.html#5863718c3b2fb1496dece10c5b745d5c" title="TODO decide if it is really needed.">dupdate</a> ( mat &amp;v,<span class="keywordtype">double</span> nu=1.0 ) {};
 <a name="l00048"></a>00048 };
 <a name="l00049"></a>00049 
-<a name="l00050"></a>00050 <span class="keyword">class </span>mEF : <span class="keyword">public</span> <a class="code" href="classmpdf.html" title="Conditional probability density, e.g. modeling some dependencies.">mpdf</a> {
-<a name="l00051"></a>00051 
-<a name="l00052"></a>00052 <span class="keyword">public</span>:
-<a name="l00053"></a>00053         mEF ( <span class="keyword">const</span> <a class="code" href="classRV.html" title="Class representing variables, most often random variables.">RV</a> &amp;rv0, <span class="keyword">const</span> <a class="code" href="classRV.html" title="Class representing variables, most often random variables.">RV</a> &amp;rvc0 ) :<a class="code" href="classmpdf.html" title="Conditional probability density, e.g. modeling some dependencies.">mpdf</a> ( rv0,rvc0 ) {};
-<a name="l00054"></a>00054 };
-<a name="l00055"></a>00055 
-<a name="l00061"></a>00061 <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt;
+<a name="l00056"></a><a class="code" href="classmEF.html">00056</a> <span class="keyword">class </span><a class="code" href="classmEF.html" title="Exponential family model.">mEF</a> : <span class="keyword">public</span> <a class="code" href="classmpdf.html" title="Conditional probability density, e.g. modeling some dependencies.">mpdf</a> {
+<a name="l00057"></a>00057 
+<a name="l00058"></a>00058 <span class="keyword">public</span>:
+<a name="l00060"></a><a class="code" href="classmEF.html#8bf51fe8654d7b83c8c8afeb19409d4f">00060</a>         <a class="code" href="classmEF.html#8bf51fe8654d7b83c8c8afeb19409d4f" title="Default constructor.">mEF</a> ( <span class="keyword">const</span> <a class="code" href="classRV.html" title="Class representing variables, most often random variables.">RV</a> &amp;rv0, <span class="keyword">const</span> <a class="code" href="classRV.html" title="Class representing variables, most often random variables.">RV</a> &amp;rvc0 ) :<a class="code" href="classmpdf.html" title="Conditional probability density, e.g. modeling some dependencies.">mpdf</a> ( rv0,rvc0 ) {};
+<a name="l00061"></a>00061 };
 <a name="l00062"></a>00062 
-<a name="l00063"></a><a class="code" href="classenorm.html">00063</a> <span class="keyword">class </span><a class="code" href="classenorm.html" title="Gaussian density with positive definite (decomposed) covariance matrix.">enorm</a> : <span class="keyword">public</span> <a class="code" href="classeEF.html" title="General conjugate exponential family posterior density.">eEF</a> {
-<a name="l00064"></a>00064 <span class="keyword">protected</span>:
-<a name="l00066"></a><a class="code" href="classenorm.html#71fde0d54bba147e00f612577f95ad20">00066</a>         vec <a class="code" href="classenorm.html#71fde0d54bba147e00f612577f95ad20" title="mean value">mu</a>;
-<a name="l00068"></a><a class="code" href="classenorm.html#4ccc8d8514d644ef1c98d8ab023748a1">00068</a>         sq_T <a class="code" href="classenorm.html#4ccc8d8514d644ef1c98d8ab023748a1" title="Covariance matrix in decomposed form.">R</a>;
-<a name="l00070"></a><a class="code" href="classenorm.html#82f39ac49911d7097f4bfe385deba355">00070</a>         sq_T <a class="code" href="classenorm.html#82f39ac49911d7097f4bfe385deba355" title="Cache: _iR = inv(R);.">_iR</a>;
-<a name="l00072"></a><a class="code" href="classenorm.html#ae12db77283a96e0f14a3eae93dc3bf1">00072</a>         <span class="keywordtype">bool</span> <a class="code" href="classenorm.html#ae12db77283a96e0f14a3eae93dc3bf1" title="indicator if _iR is chached">cached</a>;
-<a name="l00074"></a><a class="code" href="classenorm.html#6938fc390a19cdaf6ad4503fcbaada4e">00074</a>         <span class="keywordtype">int</span> <a class="code" href="classenorm.html#6938fc390a19cdaf6ad4503fcbaada4e" title="dimension (redundant from rv.count() for easier coding )">dim</a>;
-<a name="l00075"></a>00075 <span class="keyword">public</span>:
-<a name="l00076"></a>00076         <a class="code" href="classenorm.html" title="Gaussian density with positive definite (decomposed) covariance matrix.">enorm</a>() :<a class="code" href="classeEF.html" title="General conjugate exponential family posterior density.">eEF</a>() {};
-<a name="l00077"></a>00077 
-<a name="l00078"></a>00078         <a class="code" href="classenorm.html" title="Gaussian density with positive definite (decomposed) covariance matrix.">enorm</a> ( <a class="code" href="classRV.html" title="Class representing variables, most often random variables.">RV</a> &amp;rv );
-<a name="l00079"></a>00079         <span class="keywordtype">void</span> set_parameters ( <span class="keyword">const</span> vec &amp;<a class="code" href="classenorm.html#71fde0d54bba147e00f612577f95ad20" title="mean value">mu</a>,<span class="keyword">const</span> sq_T &amp;<a class="code" href="classenorm.html#4ccc8d8514d644ef1c98d8ab023748a1" title="Covariance matrix in decomposed form.">R</a> );
-<a name="l00081"></a>00081         <span class="keywordtype">void</span> <a class="code" href="classenorm.html#5b5fd142b6b17ea334597960e3fe126a" title="tupdate in exponential form (not really handy)">tupdate</a> ( <span class="keywordtype">double</span> phi, mat &amp;vbar, <span class="keywordtype">double</span> nubar );
-<a name="l00082"></a>00082         <span class="keywordtype">void</span> dupdate ( mat &amp;v,<span class="keywordtype">double</span> nu=1.0 );
-<a name="l00083"></a>00083 
-<a name="l00084"></a>00084         vec <a class="code" href="classenorm.html#6020bcd89db2c9584bd8871001bd2023" title="Returns the required moment of the epdf.">sample</a>();
-<a name="l00085"></a>00085         mat <a class="code" href="classenorm.html#6020bcd89db2c9584bd8871001bd2023" title="Returns the required moment of the epdf.">sample</a> ( <span class="keywordtype">int</span> N );
-<a name="l00086"></a>00086         <span class="keywordtype">double</span> <a class="code" href="classenorm.html#93107f05a8e9b34b64853767200121a4" title="Compute probability of argument val.">eval</a> ( <span class="keyword">const</span> vec &amp;val );
-<a name="l00087"></a>00087         <span class="keywordtype">double</span> <a class="code" href="classenorm.html#9517594915e897584eaebbb057ed8881" title="Compute log-probability of argument val.">evalpdflog</a> ( <span class="keyword">const</span> vec &amp;val );
-<a name="l00088"></a><a class="code" href="classenorm.html#191c1220c3ddd0c5f54e78f19b57ebd5">00088</a>         vec <a class="code" href="classenorm.html#191c1220c3ddd0c5f54e78f19b57ebd5" title="return expected value">mean</a>(){<span class="keywordflow">return</span> <a class="code" href="classenorm.html#71fde0d54bba147e00f612577f95ad20" title="mean value">mu</a>;}
-<a name="l00089"></a>00089 
-<a name="l00090"></a>00090 <span class="comment">//Access methods</span>
-<a name="l00092"></a><a class="code" href="classenorm.html#3be0cb541ec9b88e5aa3f60307bbc753">00092</a> <span class="comment"></span>        vec* <a class="code" href="classenorm.html#3be0cb541ec9b88e5aa3f60307bbc753" title="returns a pointer to the internal mean value. Use with Care!">_mu</a>() {<span class="keywordflow">return</span> &amp;<a class="code" href="classenorm.html#71fde0d54bba147e00f612577f95ad20" title="mean value">mu</a>;}
-<a name="l00093"></a>00093 
-<a name="l00095"></a><a class="code" href="classenorm.html#8725c534863c4fc2bddef0edfb95a740">00095</a>         <span class="keywordtype">void</span> <a class="code" href="classenorm.html#8725c534863c4fc2bddef0edfb95a740" title="returns pointers to the internal variance and its inverse. Use with Care!">_R</a> ( sq_T* &amp;pR, sq_T* &amp;piR ) {
-<a name="l00096"></a>00096                 pR=&amp;<a class="code" href="classenorm.html#4ccc8d8514d644ef1c98d8ab023748a1" title="Covariance matrix in decomposed form.">R</a>;
-<a name="l00097"></a>00097                 piR=&amp;<a class="code" href="classenorm.html#82f39ac49911d7097f4bfe385deba355" title="Cache: _iR = inv(R);.">_iR</a>;
-<a name="l00098"></a>00098         }
+<a name="l00068"></a>00068 <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt;
+<a name="l00069"></a>00069 
+<a name="l00070"></a><a class="code" href="classenorm.html">00070</a> <span class="keyword">class </span><a class="code" href="classenorm.html" title="Gaussian density with positive definite (decomposed) covariance matrix.">enorm</a> : <span class="keyword">public</span> <a class="code" href="classeEF.html" title="General conjugate exponential family posterior density.">eEF</a> {
+<a name="l00071"></a>00071 <span class="keyword">protected</span>:
+<a name="l00073"></a><a class="code" href="classenorm.html#71fde0d54bba147e00f612577f95ad20">00073</a>         vec <a class="code" href="classenorm.html#71fde0d54bba147e00f612577f95ad20" title="mean value">mu</a>;
+<a name="l00075"></a><a class="code" href="classenorm.html#4ccc8d8514d644ef1c98d8ab023748a1">00075</a>         sq_T <a class="code" href="classenorm.html#4ccc8d8514d644ef1c98d8ab023748a1" title="Covariance matrix in decomposed form.">R</a>;
+<a name="l00077"></a><a class="code" href="classenorm.html#82f39ac49911d7097f4bfe385deba355">00077</a>         sq_T <a class="code" href="classenorm.html#82f39ac49911d7097f4bfe385deba355" title="Cache: _iR = inv(R);.">_iR</a>;
+<a name="l00079"></a><a class="code" href="classenorm.html#ae12db77283a96e0f14a3eae93dc3bf1">00079</a>         <span class="keywordtype">bool</span> <a class="code" href="classenorm.html#ae12db77283a96e0f14a3eae93dc3bf1" title="indicator if _iR is chached">cached</a>;
+<a name="l00081"></a><a class="code" href="classenorm.html#6938fc390a19cdaf6ad4503fcbaada4e">00081</a>         <span class="keywordtype">int</span> <a class="code" href="classenorm.html#6938fc390a19cdaf6ad4503fcbaada4e" title="dimension (redundant from rv.count() for easier coding )">dim</a>;
+<a name="l00082"></a>00082 <span class="keyword">public</span>:
+<a name="l00083"></a>00083 <span class="comment">//      enorm() :eEF() {};</span>
+<a name="l00085"></a>00085 <span class="comment"></span>        <a class="code" href="classenorm.html#7b5cb487a2570e8109bfdc0df149aa06" title="Default constructor.">enorm</a> ( <a class="code" href="classRV.html" title="Class representing variables, most often random variables.">RV</a> &amp;<a class="code" href="classepdf.html#74da992e3f5d598da8850b646b79b9d9" title="Identified of the random variable.">rv</a> );
+<a name="l00087"></a>00087         <span class="keywordtype">void</span> <a class="code" href="classenorm.html#1394a65caa6e00d42e00cc99b12227af" title="Set mean value mu and covariance R.">set_parameters</a> ( <span class="keyword">const</span> vec &amp;<a class="code" href="classenorm.html#71fde0d54bba147e00f612577f95ad20" title="mean value">mu</a>,<span class="keyword">const</span> sq_T &amp;<a class="code" href="classenorm.html#4ccc8d8514d644ef1c98d8ab023748a1" title="Covariance matrix in decomposed form.">R</a> );
+<a name="l00089"></a>00089         <span class="keywordtype">void</span> <a class="code" href="classenorm.html#5b5fd142b6b17ea334597960e3fe126a" title="tupdate in exponential form (not really handy)">tupdate</a> ( <span class="keywordtype">double</span> phi, mat &amp;vbar, <span class="keywordtype">double</span> nubar );
+<a name="l00091"></a>00091         <span class="keywordtype">void</span> <a class="code" href="classenorm.html#5bf185e31e5954fceb90ada3debd2ff2" title="dupdate in exponential form (not really handy)">dupdate</a> ( mat &amp;v,<span class="keywordtype">double</span> nu=1.0 );
+<a name="l00092"></a>00092 
+<a name="l00093"></a>00093         vec <a class="code" href="classenorm.html#60b47544f6181ffd4530d3e415ce12c5" title="Returns the required moment of the epdf.">sample</a>() <span class="keyword">const</span>;
+<a name="l00095"></a>00095         mat <a class="code" href="classenorm.html#60b47544f6181ffd4530d3e415ce12c5" title="Returns the required moment of the epdf.">sample</a> ( <span class="keywordtype">int</span> N ) <span class="keyword">const</span>;
+<a name="l00096"></a>00096         <span class="keywordtype">double</span> <a class="code" href="classenorm.html#b9e1dfd33692d7b3f1a59f17b0e61bd0" title="Compute probability of argument val.">eval</a> ( <span class="keyword">const</span> vec &amp;val ) <span class="keyword">const</span> ;
+<a name="l00097"></a>00097         <span class="keywordtype">double</span> <a class="code" href="classenorm.html#609a7c33dbb4fdfab050f3bdd1122401" title="Compute log-probability of argument val.">evalpdflog</a> ( <span class="keyword">const</span> vec &amp;val ) <span class="keyword">const</span>;
+<a name="l00098"></a><a class="code" href="classenorm.html#50fa84da7bae02f7af17a98f37566899">00098</a>         vec <a class="code" href="classenorm.html#50fa84da7bae02f7af17a98f37566899" title="return expected value">mean</a>()<span class="keyword">const </span>{<span class="keywordflow">return</span> <a class="code" href="classenorm.html#71fde0d54bba147e00f612577f95ad20" title="mean value">mu</a>;}
 <a name="l00099"></a>00099 
-<a name="l00101"></a><a class="code" href="classenorm.html#c9ca4f2ca42568e40ca146168e7f3247">00101</a>         <span class="keywordtype">void</span> <a class="code" href="classenorm.html#c9ca4f2ca42568e40ca146168e7f3247" title="set cache as inconsistent">_cached</a> ( <span class="keywordtype">bool</span> what ) {<a class="code" href="classenorm.html#ae12db77283a96e0f14a3eae93dc3bf1" title="indicator if _iR is chached">cached</a>=what;}
-<a name="l00102"></a>00102 };
+<a name="l00100"></a>00100 <span class="comment">//Access methods</span>
+<a name="l00102"></a><a class="code" href="classenorm.html#3be0cb541ec9b88e5aa3f60307bbc753">00102</a> <span class="comment"></span>        vec* <a class="code" href="classenorm.html#3be0cb541ec9b88e5aa3f60307bbc753" title="returns a pointer to the internal mean value. Use with Care!">_mu</a>() {<span class="keywordflow">return</span> &amp;<a class="code" href="classenorm.html#71fde0d54bba147e00f612577f95ad20" title="mean value">mu</a>;}
 <a name="l00103"></a>00103 
-<a name="l00113"></a><a class="code" href="classegamma.html">00113</a> <span class="keyword">class </span><a class="code" href="classegamma.html" title="Gamma posterior density.">egamma</a> : <span class="keyword">public</span> <a class="code" href="classeEF.html" title="General conjugate exponential family posterior density.">eEF</a> {
-<a name="l00114"></a>00114 <span class="keyword">protected</span>:
-<a name="l00115"></a>00115         vec alpha;
-<a name="l00116"></a>00116         vec beta;
-<a name="l00117"></a>00117 <span class="keyword">public</span> :
-<a name="l00119"></a><a class="code" href="classegamma.html#4b1d34f3b244ea51a58ec10c468788c1">00119</a>         <a class="code" href="classegamma.html#4b1d34f3b244ea51a58ec10c468788c1" title="Default constructor.">egamma</a> ( <span class="keyword">const</span> <a class="code" href="classRV.html" title="Class representing variables, most often random variables.">RV</a> &amp;rv ) :<a class="code" href="classeEF.html" title="General conjugate exponential family posterior density.">eEF</a> ( rv ) {};
-<a name="l00121"></a><a class="code" href="classegamma.html#8e348b89be82b70471fe8c5630f61339">00121</a>         <span class="keywordtype">void</span> <a class="code" href="classegamma.html#8e348b89be82b70471fe8c5630f61339" title="Sets parameters.">set_parameters</a> ( <span class="keyword">const</span> vec &amp;a, <span class="keyword">const</span> vec &amp;b ) {alpha=a,beta=b;};
-<a name="l00122"></a>00122         vec <a class="code" href="classegamma.html#0a2186a586432c2c3f22d09c5341890f" title="Returns the required moment of the epdf.">sample</a>();
-<a name="l00123"></a>00123         mat <a class="code" href="classegamma.html#0a2186a586432c2c3f22d09c5341890f" title="Returns the required moment of the epdf.">sample</a> ( <span class="keywordtype">int</span> N );
-<a name="l00124"></a>00124         <span class="keywordtype">double</span> evalpdflog ( <span class="keyword">const</span> vec val );
-<a name="l00126"></a><a class="code" href="classegamma.html#44445c56e60b91b377f207f8d5089790">00126</a>         <span class="keywordtype">void</span> <a class="code" href="classegamma.html#44445c56e60b91b377f207f8d5089790" title="Returns poiter to alpha and beta. Potentially dengerous: use with care!">_param</a> ( vec* &amp;a, vec* &amp;b ) {a=&amp;alpha;b=&amp;beta;};
-<a name="l00127"></a><a class="code" href="classegamma.html#6617890ffa40767fc196876534d4119d">00127</a>         vec <a class="code" href="classegamma.html#6617890ffa40767fc196876534d4119d" title="return expected value">mean</a>(){vec pom(alpha); pom/=beta; <span class="keywordflow">return</span> pom;}
-<a name="l00128"></a>00128 };
-<a name="l00129"></a>00129 
-<a name="l00131"></a><a class="code" href="classemix.html">00131</a> <span class="keyword">class </span><a class="code" href="classemix.html" title="Weighted mixture of epdfs with external owned components.">emix</a> : <span class="keyword">public</span> <a class="code" href="classepdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a> {
-<a name="l00132"></a>00132 <span class="keyword">protected</span>:
-<a name="l00133"></a>00133         <span class="keywordtype">int</span> n;
-<a name="l00134"></a>00134         vec &amp;w;
-<a name="l00135"></a>00135         Array&lt;epdf*&gt; Coms;
-<a name="l00136"></a>00136 <span class="keyword">public</span>:
-<a name="l00138"></a><a class="code" href="classemix.html#b0ac204af8919c22bce72816ed82019e">00138</a>         <a class="code" href="classemix.html#b0ac204af8919c22bce72816ed82019e" title="Default constructor.">emix</a> ( <span class="keyword">const</span> <a class="code" href="classRV.html" title="Class representing variables, most often random variables.">RV</a> &amp;rv, vec &amp;w0): <a class="code" href="classepdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a>(rv), n(w0.length()), w(w0), Coms(n) {};
-<a name="l00139"></a>00139         <span class="keywordtype">void</span> set_parameters( <span class="keywordtype">int</span> &amp;i, <a class="code" href="classepdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a>* ep){Coms(i)=ep;}
-<a name="l00140"></a><a class="code" href="classemix.html#dcb3f927bf061eac6229850158ca1558">00140</a>         vec <a class="code" href="classemix.html#dcb3f927bf061eac6229850158ca1558" title="return expected value">mean</a>(){vec pom; <span class="keywordflow">for</span>(<span class="keywordtype">int</span> i=0;i&lt;n;i++){pom+=Coms(i)-&gt;mean()*w(i);} <span class="keywordflow">return</span> pom;};
-<a name="l00141"></a><a class="code" href="classemix.html#3eb9a8e12ce1c5c8a3ddb245354b6941">00141</a>         vec <a class="code" href="classemix.html#3eb9a8e12ce1c5c8a3ddb245354b6941" title="Returns the required moment of the epdf.">sample</a>() {it_error ( <span class="stringliteral">"Not implemented"</span> );<span class="keywordflow">return</span> 0;}
-<a name="l00142"></a>00142 };
-<a name="l00143"></a>00143 
-<a name="l00145"></a>00145 
-<a name="l00146"></a><a class="code" href="classeuni.html">00146</a> <span class="keyword">class </span><a class="code" href="classeuni.html" title="Uniform distributed density on a rectangular support.">euni</a>: <span class="keyword">public</span> <a class="code" href="classepdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a> {
-<a name="l00147"></a>00147 <span class="keyword">protected</span>:
-<a name="l00149"></a><a class="code" href="classeuni.html#ef42cd8d7645422048d46c46ec5cdac1">00149</a>         vec <a class="code" href="classeuni.html#ef42cd8d7645422048d46c46ec5cdac1" title="lower bound on support">low</a>;
-<a name="l00151"></a><a class="code" href="classeuni.html#71b6d6b41aeb61a7f76f682b72119231">00151</a>         vec <a class="code" href="classeuni.html#71b6d6b41aeb61a7f76f682b72119231" title="upper bound on support">high</a>;
-<a name="l00153"></a><a class="code" href="classeuni.html#52a6ff4a54010f88a6a19fca605c64a4">00153</a>         vec <a class="code" href="classeuni.html#52a6ff4a54010f88a6a19fca605c64a4" title="internal">distance</a>;
-<a name="l00155"></a><a class="code" href="classeuni.html#63105490e946e43372d6187ad1bafdda">00155</a>         <span class="keywordtype">double</span> <a class="code" href="classeuni.html#63105490e946e43372d6187ad1bafdda" title="normalizing coefficients">nk</a>,lnk;
-<a name="l00156"></a>00156 <span class="keyword">public</span>:
-<a name="l00157"></a>00157         <a class="code" href="classeuni.html" title="Uniform distributed density on a rectangular support.">euni</a> ( <span class="keyword">const</span> <a class="code" href="classRV.html" title="Class representing variables, most often random variables.">RV</a> rv ) :<a class="code" href="classepdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a> ( rv ) {}
-<a name="l00158"></a><a class="code" href="classeuni.html#95f29237feb32fcadf570a181d5a0918">00158</a>         <span class="keywordtype">double</span> <a class="code" href="classeuni.html#95f29237feb32fcadf570a181d5a0918" title="Compute probability of argument val.">eval</a> ( <span class="keyword">const</span> vec &amp;val ) {<span class="keywordflow">return</span> <a class="code" href="classeuni.html#63105490e946e43372d6187ad1bafdda" title="normalizing coefficients">nk</a>;}
-<a name="l00159"></a><a class="code" href="classeuni.html#830fca2ffb6786530529c57eabc81666">00159</a>         <span class="keywordtype">double</span> <a class="code" href="classeuni.html#830fca2ffb6786530529c57eabc81666" title="Compute log-probability of argument val.">evalpdflog</a> ( <span class="keyword">const</span> vec &amp;val ) {<span class="keywordflow">return</span> lnk;}
-<a name="l00160"></a><a class="code" href="classeuni.html#0f71562e3e919aba823cb7d9d420ad4c">00160</a>         vec <a class="code" href="classeuni.html#0f71562e3e919aba823cb7d9d420ad4c" title="Returns the required moment of the epdf.">sample</a>() {
-<a name="l00161"></a>00161                 vec smp ( rv.<a class="code" href="classRV.html#f5c7b8bd589eef09ccdf3329a0addea0" title="Return length (number of scalars) of the RV.">count</a>() ); UniRNG.sample_vector ( rv.<a class="code" href="classRV.html#f5c7b8bd589eef09ccdf3329a0addea0" title="Return length (number of scalars) of the RV.">count</a>(),smp );
-<a name="l00162"></a>00162                 <span class="keywordflow">return</span> <a class="code" href="classeuni.html#ef42cd8d7645422048d46c46ec5cdac1" title="lower bound on support">low</a>+<a class="code" href="classeuni.html#52a6ff4a54010f88a6a19fca605c64a4" title="internal">distance</a>*smp;
-<a name="l00163"></a>00163         }
-<a name="l00164"></a>00164         <span class="keywordtype">void</span> set_parameters ( <span class="keyword">const</span> vec &amp;low0, <span class="keyword">const</span> vec &amp;high0 ) {
-<a name="l00165"></a>00165                 <a class="code" href="classeuni.html#52a6ff4a54010f88a6a19fca605c64a4" title="internal">distance</a> = high0-low0;
-<a name="l00166"></a>00166                 it_assert_debug ( min ( <a class="code" href="classeuni.html#52a6ff4a54010f88a6a19fca605c64a4" title="internal">distance</a> ) &gt;0.0,<span class="stringliteral">"bad support"</span> );
-<a name="l00167"></a>00167                 <a class="code" href="classeuni.html#ef42cd8d7645422048d46c46ec5cdac1" title="lower bound on support">low</a> = low0;
-<a name="l00168"></a>00168                 <a class="code" href="classeuni.html#71b6d6b41aeb61a7f76f682b72119231" title="upper bound on support">high</a> = high0;
-<a name="l00169"></a>00169                 <a class="code" href="classeuni.html#63105490e946e43372d6187ad1bafdda" title="normalizing coefficients">nk</a> = prod ( 1.0/<a class="code" href="classeuni.html#52a6ff4a54010f88a6a19fca605c64a4" title="internal">distance</a> );
-<a name="l00170"></a>00170                 lnk = log ( <a class="code" href="classeuni.html#63105490e946e43372d6187ad1bafdda" title="normalizing coefficients">nk</a> );
-<a name="l00171"></a>00171         }
-<a name="l00172"></a><a class="code" href="classeuni.html#34a2f2d23158c40b97b44dfe551a1b3d">00172</a>         vec <a class="code" href="classeuni.html#34a2f2d23158c40b97b44dfe551a1b3d" title="return expected value">mean</a>(){vec pom=<a class="code" href="classeuni.html#71b6d6b41aeb61a7f76f682b72119231" title="upper bound on support">high</a>; pom-=<a class="code" href="classeuni.html#ef42cd8d7645422048d46c46ec5cdac1" title="lower bound on support">low</a>; pom/=2.0; <span class="keywordflow">return</span> pom;}
-<a name="l00173"></a>00173 };
-<a name="l00174"></a>00174 
-<a name="l00175"></a>00175 
-<a name="l00181"></a>00181 <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt;
-<a name="l00182"></a><a class="code" href="classmlnorm.html">00182</a> <span class="keyword">class </span><a class="code" href="classmlnorm.html" title="Normal distributed linear function with linear function of mean value;.">mlnorm</a> : <span class="keyword">public</span> mEF {
-<a name="l00183"></a>00183         <a class="code" href="classenorm.html" title="Gaussian density with positive definite (decomposed) covariance matrix.">enorm&lt;sq_T&gt;</a> <a class="code" href="classepdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a>;
-<a name="l00184"></a>00184         vec* _mu; <span class="comment">//cached epdf.mu;</span>
-<a name="l00185"></a>00185         mat A;
-<a name="l00186"></a>00186 <span class="keyword">public</span>:
-<a name="l00188"></a>00188         <a class="code" href="classmlnorm.html#f927203b3f31171c5c10ffc7caa797f5" title="Constructor.">mlnorm</a> ( <a class="code" href="classRV.html" title="Class representing variables, most often random variables.">RV</a> &amp;rv,<a class="code" href="classRV.html" title="Class representing variables, most often random variables.">RV</a> &amp;rvc );
-<a name="l00189"></a>00189         <span class="keywordtype">void</span> set_parameters ( <span class="keyword">const</span>  mat &amp;A, <span class="keyword">const</span> sq_T &amp;R );
-<a name="l00191"></a>00191         vec <a class="code" href="classmlnorm.html#decf3e3b5c8e0812e5b4dbe94fa2ae18" title="Generate one sample of the posterior.">samplecond</a> ( vec &amp;cond, <span class="keywordtype">double</span> &amp;lik );
-<a name="l00193"></a>00193         mat <a class="code" href="classmlnorm.html#decf3e3b5c8e0812e5b4dbe94fa2ae18" title="Generate one sample of the posterior.">samplecond</a> ( vec &amp;cond, vec &amp;lik, <span class="keywordtype">int</span> n );
-<a name="l00194"></a>00194         <span class="keywordtype">void</span> condition ( vec &amp;cond );
-<a name="l00195"></a>00195 };
-<a name="l00196"></a>00196 
-<a name="l00206"></a><a class="code" href="classmgamma.html">00206</a> <span class="keyword">class </span><a class="code" href="classmgamma.html" title="Gamma random walk.">mgamma</a> : <span class="keyword">public</span> mEF {
-<a name="l00207"></a>00207         <a class="code" href="classegamma.html" title="Gamma posterior density.">egamma</a> <a class="code" href="classepdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a>;
-<a name="l00208"></a>00208         <span class="keywordtype">double</span> k;
-<a name="l00209"></a>00209         vec* _beta;
-<a name="l00210"></a>00210 
-<a name="l00211"></a>00211 <span class="keyword">public</span>:
-<a name="l00213"></a>00213         <a class="code" href="classmgamma.html#af43e61b86900c0398d5c0ffc83b94e6" title="Constructor.">mgamma</a> ( <span class="keyword">const</span> <a class="code" href="classRV.html" title="Class representing variables, most often random variables.">RV</a> &amp;rv,<span class="keyword">const</span> <a class="code" href="classRV.html" title="Class representing variables, most often random variables.">RV</a> &amp;rvc );
-<a name="l00214"></a>00214         <span class="keywordtype">void</span> set_parameters ( <span class="keywordtype">double</span> k );
-<a name="l00216"></a>00216         vec <a class="code" href="classmgamma.html#9f40dc43885085fad8e3d6652b79e139" title="Generate one sample of the posterior.">samplecond</a> ( vec &amp;cond, <span class="keywordtype">double</span> &amp;lik );
-<a name="l00218"></a>00218         mat <a class="code" href="classmgamma.html#9f40dc43885085fad8e3d6652b79e139" title="Generate one sample of the posterior.">samplecond</a> ( vec &amp;cond, vec &amp;lik, <span class="keywordtype">int</span> n );
-<a name="l00219"></a>00219         <span class="keywordtype">void</span> condition ( <span class="keyword">const</span> vec &amp;val ) {*_beta=k/val;};
-<a name="l00220"></a>00220 };
-<a name="l00221"></a>00221 
-<a name="l00223"></a><a class="code" href="libEF_8h.html#99497a3ff630f761cf6bff7babd23212">00223</a> <span class="keyword">enum</span> <a class="code" href="libEF_8h.html#99497a3ff630f761cf6bff7babd23212" title="Switch between various resampling methods.">RESAMPLING_METHOD</a> { MULTINOMIAL = 0, STRATIFIED = 1, SYSTEMATIC = 3 };
-<a name="l00229"></a><a class="code" href="classeEmp.html">00229</a> <span class="keyword">class </span><a class="code" href="classeEmp.html" title="Weighted empirical density.">eEmp</a>: <span class="keyword">public</span> <a class="code" href="classepdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a> {
-<a name="l00230"></a>00230 <span class="keyword">protected</span> :
-<a name="l00232"></a><a class="code" href="classeEmp.html#8c33034de0e35f03f8bb85d3d67438fd">00232</a>         <span class="keywordtype">int</span> <a class="code" href="classeEmp.html#8c33034de0e35f03f8bb85d3d67438fd" title="Number of particles.">n</a>;
-<a name="l00233"></a>00233         vec w;
-<a name="l00234"></a>00234         Array&lt;vec&gt; samples;
+<a name="l00105"></a><a class="code" href="classenorm.html#8725c534863c4fc2bddef0edfb95a740">00105</a>         <span class="keywordtype">void</span> <a class="code" href="classenorm.html#8725c534863c4fc2bddef0edfb95a740" title="returns pointers to the internal variance and its inverse. Use with Care!">_R</a> ( sq_T* &amp;pR, sq_T* &amp;piR ) {
+<a name="l00106"></a>00106                 pR=&amp;<a class="code" href="classenorm.html#4ccc8d8514d644ef1c98d8ab023748a1" title="Covariance matrix in decomposed form.">R</a>;
+<a name="l00107"></a>00107                 piR=&amp;<a class="code" href="classenorm.html#82f39ac49911d7097f4bfe385deba355" title="Cache: _iR = inv(R);.">_iR</a>;
+<a name="l00108"></a>00108         }
+<a name="l00109"></a>00109 
+<a name="l00111"></a><a class="code" href="classenorm.html#c9ca4f2ca42568e40ca146168e7f3247">00111</a>         <span class="keywordtype">void</span> <a class="code" href="classenorm.html#c9ca4f2ca42568e40ca146168e7f3247" title="set cache as inconsistent">_cached</a> ( <span class="keywordtype">bool</span> what ) {<a class="code" href="classenorm.html#ae12db77283a96e0f14a3eae93dc3bf1" title="indicator if _iR is chached">cached</a>=what;}
+<a name="l00112"></a>00112 };
+<a name="l00113"></a>00113 
+<a name="l00123"></a><a class="code" href="classegamma.html">00123</a> <span class="keyword">class </span><a class="code" href="classegamma.html" title="Gamma posterior density.">egamma</a> : <span class="keyword">public</span> <a class="code" href="classeEF.html" title="General conjugate exponential family posterior density.">eEF</a> {
+<a name="l00124"></a>00124 <span class="keyword">protected</span>:
+<a name="l00126"></a><a class="code" href="classegamma.html#376cebd8932546c440f21b182910b01b">00126</a>         vec <a class="code" href="classegamma.html#376cebd8932546c440f21b182910b01b" title="Vector .">alpha</a>;
+<a name="l00128"></a><a class="code" href="classegamma.html#cfc5f136467488a421ab22f886323790">00128</a>         vec <a class="code" href="classegamma.html#cfc5f136467488a421ab22f886323790" title="Vector .">beta</a>;
+<a name="l00129"></a>00129 <span class="keyword">public</span> :
+<a name="l00131"></a><a class="code" href="classegamma.html#4b1d34f3b244ea51a58ec10c468788c1">00131</a>         <a class="code" href="classegamma.html#4b1d34f3b244ea51a58ec10c468788c1" title="Default constructor.">egamma</a> ( <span class="keyword">const</span> <a class="code" href="classRV.html" title="Class representing variables, most often random variables.">RV</a> &amp;<a class="code" href="classepdf.html#74da992e3f5d598da8850b646b79b9d9" title="Identified of the random variable.">rv</a> ) :<a class="code" href="classeEF.html" title="General conjugate exponential family posterior density.">eEF</a> ( rv ) {};
+<a name="l00133"></a><a class="code" href="classegamma.html#8e348b89be82b70471fe8c5630f61339">00133</a>         <span class="keywordtype">void</span> <a class="code" href="classegamma.html#8e348b89be82b70471fe8c5630f61339" title="Sets parameters.">set_parameters</a> ( <span class="keyword">const</span> vec &amp;a, <span class="keyword">const</span> vec &amp;b ) {<a class="code" href="classegamma.html#376cebd8932546c440f21b182910b01b" title="Vector .">alpha</a>=a,<a class="code" href="classegamma.html#cfc5f136467488a421ab22f886323790" title="Vector .">beta</a>=b;};
+<a name="l00134"></a>00134         vec <a class="code" href="classegamma.html#8e10c0021b5dfdd9cb62c6959b5ef425" title="Returns the required moment of the epdf.">sample</a>() <span class="keyword">const</span>;
+<a name="l00136"></a>00136         mat <a class="code" href="classegamma.html#8e10c0021b5dfdd9cb62c6959b5ef425" title="Returns the required moment of the epdf.">sample</a> ( <span class="keywordtype">int</span> N ) <span class="keyword">const</span>;
+<a name="l00137"></a>00137         <span class="keywordtype">double</span> <a class="code" href="classegamma.html#de84faac8f9799dfe2777ddbedf997ef" title="Compute log-probability of argument val.">evalpdflog</a> ( <span class="keyword">const</span> vec &amp;val ) <span class="keyword">const</span>;
+<a name="l00139"></a><a class="code" href="classegamma.html#44445c56e60b91b377f207f8d5089790">00139</a>         <span class="keywordtype">void</span> <a class="code" href="classegamma.html#44445c56e60b91b377f207f8d5089790" title="Returns poiter to alpha and beta. Potentially dengerous: use with care!">_param</a> ( vec* &amp;a, vec* &amp;b ) {a=&amp;<a class="code" href="classegamma.html#376cebd8932546c440f21b182910b01b" title="Vector .">alpha</a>;b=&amp;<a class="code" href="classegamma.html#cfc5f136467488a421ab22f886323790" title="Vector .">beta</a>;};
+<a name="l00140"></a><a class="code" href="classegamma.html#6ab5ba56f7cdb2e5921c3e77524fa50a">00140</a>         vec <a class="code" href="classegamma.html#6ab5ba56f7cdb2e5921c3e77524fa50a" title="return expected value">mean</a>()<span class="keyword">const </span>{vec pom(<a class="code" href="classegamma.html#376cebd8932546c440f21b182910b01b" title="Vector .">alpha</a>); pom/=<a class="code" href="classegamma.html#cfc5f136467488a421ab22f886323790" title="Vector .">beta</a>; <span class="keywordflow">return</span> pom;}
+<a name="l00141"></a>00141 };
+<a name="l00142"></a>00142 <span class="comment">/*</span>
+<a name="l00144"></a>00144 <span class="comment">class emix : public epdf {</span>
+<a name="l00145"></a>00145 <span class="comment">protected:</span>
+<a name="l00146"></a>00146 <span class="comment">        int n;</span>
+<a name="l00147"></a>00147 <span class="comment">        vec &amp;w;</span>
+<a name="l00148"></a>00148 <span class="comment">        Array&lt;epdf*&gt; Coms;</span>
+<a name="l00149"></a>00149 <span class="comment">public:</span>
+<a name="l00151"></a>00151 <span class="comment">        emix ( const RV &amp;rv, vec &amp;w0): epdf(rv), n(w0.length()), w(w0), Coms(n) {};</span>
+<a name="l00152"></a>00152 <span class="comment">        void set_parameters( int &amp;i, double wi, epdf* ep){w(i)=wi;Coms(i)=ep;}</span>
+<a name="l00153"></a>00153 <span class="comment">        vec mean(){vec pom; for(int i=0;i&lt;n;i++){pom+=Coms(i)-&gt;mean()*w(i);} return pom;};</span>
+<a name="l00154"></a>00154 <span class="comment">        vec sample() {it_error ( "Not implemented" );return 0;}</span>
+<a name="l00155"></a>00155 <span class="comment">};</span>
+<a name="l00156"></a>00156 <span class="comment">*/</span>
+<a name="l00157"></a>00157 
+<a name="l00159"></a>00159 
+<a name="l00160"></a><a class="code" href="classeuni.html">00160</a> <span class="keyword">class </span><a class="code" href="classeuni.html" title="Uniform distributed density on a rectangular support.">euni</a>: <span class="keyword">public</span> <a class="code" href="classepdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a> {
+<a name="l00161"></a>00161 <span class="keyword">protected</span>:
+<a name="l00163"></a><a class="code" href="classeuni.html#ef42cd8d7645422048d46c46ec5cdac1">00163</a>         vec <a class="code" href="classeuni.html#ef42cd8d7645422048d46c46ec5cdac1" title="lower bound on support">low</a>;
+<a name="l00165"></a><a class="code" href="classeuni.html#71b6d6b41aeb61a7f76f682b72119231">00165</a>         vec <a class="code" href="classeuni.html#71b6d6b41aeb61a7f76f682b72119231" title="upper bound on support">high</a>;
+<a name="l00167"></a><a class="code" href="classeuni.html#52a6ff4a54010f88a6a19fca605c64a4">00167</a>         vec <a class="code" href="classeuni.html#52a6ff4a54010f88a6a19fca605c64a4" title="internal">distance</a>;
+<a name="l00169"></a><a class="code" href="classeuni.html#63105490e946e43372d6187ad1bafdda">00169</a>         <span class="keywordtype">double</span> <a class="code" href="classeuni.html#63105490e946e43372d6187ad1bafdda" title="normalizing coefficients">nk</a>;
+<a name="l00171"></a><a class="code" href="classeuni.html#f445a0ce24f39d14c1a4eed53fc8e2c3">00171</a>         <span class="keywordtype">double</span> <a class="code" href="classeuni.html#f445a0ce24f39d14c1a4eed53fc8e2c3" title="cache of log( nk )">lnk</a>;
+<a name="l00172"></a>00172 <span class="keyword">public</span>:
+<a name="l00174"></a><a class="code" href="classeuni.html#2537a6c239cff52e3ba814851a1116cd">00174</a>         <a class="code" href="classeuni.html#2537a6c239cff52e3ba814851a1116cd" title="Defualt constructor.">euni</a> ( <span class="keyword">const</span> <a class="code" href="classRV.html" title="Class representing variables, most often random variables.">RV</a> <a class="code" href="classepdf.html#74da992e3f5d598da8850b646b79b9d9" title="Identified of the random variable.">rv</a> ) :<a class="code" href="classepdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a> ( rv ) {}
+<a name="l00175"></a><a class="code" href="classeuni.html#2723d4992900b5c5495bfa03628195ed">00175</a>         <span class="keywordtype">double</span> <a class="code" href="classeuni.html#2723d4992900b5c5495bfa03628195ed" title="Compute probability of argument val.">eval</a> ( <span class="keyword">const</span> vec &amp;val )<span class="keyword"> const  </span>{<span class="keywordflow">return</span> <a class="code" href="classeuni.html#63105490e946e43372d6187ad1bafdda" title="normalizing coefficients">nk</a>;}
+<a name="l00176"></a><a class="code" href="classeuni.html#06af95d514a6623ad4688bd2ad50ad71">00176</a>         <span class="keywordtype">double</span> <a class="code" href="classeuni.html#06af95d514a6623ad4688bd2ad50ad71" title="Compute log-probability of argument val.">evalpdflog</a> ( <span class="keyword">const</span> vec &amp;val )<span class="keyword"> const  </span>{<span class="keywordflow">return</span> <a class="code" href="classeuni.html#f445a0ce24f39d14c1a4eed53fc8e2c3" title="cache of log( nk )">lnk</a>;}
+<a name="l00177"></a><a class="code" href="classeuni.html#4a0e09392be17beaee120ba98fc038cd">00177</a>         vec <a class="code" href="classeuni.html#4a0e09392be17beaee120ba98fc038cd" title="Returns the required moment of the epdf.">sample</a>()<span class="keyword"> const </span>{
+<a name="l00178"></a>00178                 vec smp ( <a class="code" href="classepdf.html#74da992e3f5d598da8850b646b79b9d9" title="Identified of the random variable.">rv</a>.<a class="code" href="classRV.html#f5c7b8bd589eef09ccdf3329a0addea0" title="Return length (number of scalars) of the RV.">count</a>() ); UniRNG.sample_vector ( <a class="code" href="classepdf.html#74da992e3f5d598da8850b646b79b9d9" title="Identified of the random variable.">rv</a>.<a class="code" href="classRV.html#f5c7b8bd589eef09ccdf3329a0addea0" title="Return length (number of scalars) of the RV.">count</a>(),smp );
+<a name="l00179"></a>00179                 <span class="keywordflow">return</span> <a class="code" href="classeuni.html#ef42cd8d7645422048d46c46ec5cdac1" title="lower bound on support">low</a>+<a class="code" href="classeuni.html#52a6ff4a54010f88a6a19fca605c64a4" title="internal">distance</a>*smp;
+<a name="l00180"></a>00180         }
+<a name="l00182"></a><a class="code" href="classeuni.html#4fd7c6a05100616ad16ece405cad7bf2">00182</a>         <span class="keywordtype">void</span> <a class="code" href="classeuni.html#4fd7c6a05100616ad16ece405cad7bf2" title="set values of low and high ">set_parameters</a> ( <span class="keyword">const</span> vec &amp;low0, <span class="keyword">const</span> vec &amp;high0 ) {
+<a name="l00183"></a>00183                 <a class="code" href="classeuni.html#52a6ff4a54010f88a6a19fca605c64a4" title="internal">distance</a> = high0-low0;
+<a name="l00184"></a>00184                 it_assert_debug ( min ( <a class="code" href="classeuni.html#52a6ff4a54010f88a6a19fca605c64a4" title="internal">distance</a> ) &gt;0.0,<span class="stringliteral">"bad support"</span> );
+<a name="l00185"></a>00185                 <a class="code" href="classeuni.html#ef42cd8d7645422048d46c46ec5cdac1" title="lower bound on support">low</a> = low0;
+<a name="l00186"></a>00186                 <a class="code" href="classeuni.html#71b6d6b41aeb61a7f76f682b72119231" title="upper bound on support">high</a> = high0;
+<a name="l00187"></a>00187                 <a class="code" href="classeuni.html#63105490e946e43372d6187ad1bafdda" title="normalizing coefficients">nk</a> = prod ( 1.0/<a class="code" href="classeuni.html#52a6ff4a54010f88a6a19fca605c64a4" title="internal">distance</a> );
+<a name="l00188"></a>00188                 <a class="code" href="classeuni.html#f445a0ce24f39d14c1a4eed53fc8e2c3" title="cache of log( nk )">lnk</a> = log ( <a class="code" href="classeuni.html#63105490e946e43372d6187ad1bafdda" title="normalizing coefficients">nk</a> );
+<a name="l00189"></a>00189         }
+<a name="l00190"></a><a class="code" href="classeuni.html#8050087e421a9cfd1b4b1f8bd33b1cc1">00190</a>         vec <a class="code" href="classeuni.html#8050087e421a9cfd1b4b1f8bd33b1cc1" title="return expected value">mean</a>()<span class="keyword"> const </span>{vec pom=<a class="code" href="classeuni.html#71b6d6b41aeb61a7f76f682b72119231" title="upper bound on support">high</a>; pom-=<a class="code" href="classeuni.html#ef42cd8d7645422048d46c46ec5cdac1" title="lower bound on support">low</a>; pom/=2.0; <span class="keywordflow">return</span> pom;}
+<a name="l00191"></a>00191 };
+<a name="l00192"></a>00192 
+<a name="l00193"></a>00193 
+<a name="l00199"></a>00199 <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt;
+<a name="l00200"></a><a class="code" href="classmlnorm.html">00200</a> <span class="keyword">class </span><a class="code" href="classmlnorm.html" title="Normal distributed linear function with linear function of mean value;.">mlnorm</a> : <span class="keyword">public</span> <a class="code" href="classmEF.html" title="Exponential family model.">mEF</a> {
+<a name="l00202"></a>00202         <a class="code" href="classenorm.html" title="Gaussian density with positive definite (decomposed) covariance matrix.">enorm&lt;sq_T&gt;</a> <a class="code" href="classepdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a>;
+<a name="l00203"></a>00203         vec* _mu; <span class="comment">//cached epdf.mu;</span>
+<a name="l00204"></a>00204         mat A;
+<a name="l00205"></a>00205 <span class="keyword">public</span>:
+<a name="l00207"></a>00207         <a class="code" href="classmlnorm.html#f927203b3f31171c5c10ffc7caa797f5" title="Constructor.">mlnorm</a> ( <a class="code" href="classRV.html" title="Class representing variables, most often random variables.">RV</a> &amp;<a class="code" href="classmpdf.html#f6687c07ff07d47812dd565368ca59eb" title="modeled random variable">rv</a>,<a class="code" href="classRV.html" title="Class representing variables, most often random variables.">RV</a> &amp;<a class="code" href="classmpdf.html#acb7dda792b3cd5576f39fa3129abbab" title="random variable in condition">rvc</a> );
+<a name="l00209"></a>00209         <span class="keywordtype">void</span> <a class="code" href="classmlnorm.html#b6749030c5d5abcb3eb6898f74cea3c0" title="Set A and R.">set_parameters</a> ( <span class="keyword">const</span>  mat &amp;A, <span class="keyword">const</span> sq_T &amp;R );
+<a name="l00211"></a>00211         vec <a class="code" href="classmlnorm.html#decf3e3b5c8e0812e5b4dbe94fa2ae18" title="Generate one sample of the posterior.">samplecond</a> ( vec &amp;cond, <span class="keywordtype">double</span> &amp;lik );
+<a name="l00213"></a>00213         mat <a class="code" href="classmlnorm.html#decf3e3b5c8e0812e5b4dbe94fa2ae18" title="Generate one sample of the posterior.">samplecond</a> ( vec &amp;cond, vec &amp;lik, <span class="keywordtype">int</span> n );
+<a name="l00215"></a>00215         <span class="keywordtype">void</span> <a class="code" href="classmlnorm.html#5232fc7e305eceab4e2bd6a8daa44195" title="Set value of rvc . Result of this operation is stored in epdf use function _ep to...">condition</a> ( vec &amp;cond );
+<a name="l00216"></a>00216 };
+<a name="l00217"></a>00217 
+<a name="l00227"></a><a class="code" href="classmgamma.html">00227</a> <span class="keyword">class </span><a class="code" href="classmgamma.html" title="Gamma random walk.">mgamma</a> : <span class="keyword">public</span> <a class="code" href="classmEF.html" title="Exponential family model.">mEF</a> {
+<a name="l00229"></a>00229         <a class="code" href="classegamma.html" title="Gamma posterior density.">egamma</a> <a class="code" href="classepdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a>;
+<a name="l00231"></a>00231         <span class="keywordtype">double</span> k;
+<a name="l00233"></a>00233         vec* _beta;
+<a name="l00234"></a>00234 
 <a name="l00235"></a>00235 <span class="keyword">public</span>:
-<a name="l00236"></a>00236         <a class="code" href="classeEmp.html" title="Weighted empirical density.">eEmp</a> ( <span class="keyword">const</span> <a class="code" href="classRV.html" title="Class representing variables, most often random variables.">RV</a> &amp;rv0 ) :<a class="code" href="classepdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a> ( rv0 ) {};
-<a name="l00237"></a>00237         <span class="keywordtype">void</span> set_parameters ( <span class="keyword">const</span> vec &amp;w0, <a class="code" href="classepdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a>* pdf0 );
-<a name="l00239"></a><a class="code" href="classeEmp.html#31b2bfb73b72486a5c89f2ab850c7a9b">00239</a>         vec&amp; <a class="code" href="classeEmp.html#31b2bfb73b72486a5c89f2ab850c7a9b" title="Potentially dangerous, use with care.">_w</a>()  {<span class="keywordflow">return</span> w;};
-<a name="l00240"></a>00240         Array&lt;vec&gt;&amp; _samples() {<span class="keywordflow">return</span> samples;};
-<a name="l00242"></a>00242         ivec <a class="code" href="classeEmp.html#77268292fc4465cb73ddbfb1f2932a59" title="Function performs resampling, i.e. removal of low-weight samples and duplication...">resample</a> ( <a class="code" href="libEF_8h.html#99497a3ff630f761cf6bff7babd23212" title="Switch between various resampling methods.">RESAMPLING_METHOD</a> method = SYSTEMATIC );
-<a name="l00243"></a><a class="code" href="classeEmp.html#c9b44099a400579b88aff9f5afaf9c13">00243</a>         vec <a class="code" href="classeEmp.html#c9b44099a400579b88aff9f5afaf9c13" title="Returns the required moment of the epdf.">sample</a>() {it_error ( <span class="stringliteral">"Not implemented"</span> );<span class="keywordflow">return</span> 0;}
-<a name="l00244"></a><a class="code" href="classeEmp.html#de42454cb65a17fd8662d207dd59aacf">00244</a>         vec <a class="code" href="classeEmp.html#de42454cb65a17fd8662d207dd59aacf" title="return expected value">mean</a>(){vec pom; 
-<a name="l00245"></a>00245                 <span class="keywordflow">for</span> (<span class="keywordtype">int</span> i=0;i&lt;<a class="code" href="classeEmp.html#8c33034de0e35f03f8bb85d3d67438fd" title="Number of particles.">n</a>;i++){pom+=samples(i)*w(i);}
-<a name="l00246"></a>00246                 <span class="keywordflow">return</span> pom;
-<a name="l00247"></a>00247         }
-<a name="l00248"></a>00248 };
-<a name="l00249"></a>00249 
-<a name="l00250"></a>00250 
-<a name="l00252"></a>00252 
-<a name="l00253"></a>00253 <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt;
-<a name="l00254"></a>00254 <a class="code" href="classenorm.html" title="Gaussian density with positive definite (decomposed) covariance matrix.">enorm&lt;sq_T&gt;::enorm</a> ( <a class="code" href="classRV.html" title="Class representing variables, most often random variables.">RV</a> &amp;rv ) :<a class="code" href="classeEF.html" title="General conjugate exponential family posterior density.">eEF</a>(), mu ( rv.count() ),R ( rv.count() ),_iR ( rv.count() ),cached ( false ),dim ( rv.count() ) {};
-<a name="l00255"></a>00255 
-<a name="l00256"></a>00256 <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt;
-<a name="l00257"></a>00257 <span class="keywordtype">void</span> <a class="code" href="classenorm.html" title="Gaussian density with positive definite (decomposed) covariance matrix.">enorm&lt;sq_T&gt;::set_parameters</a> ( <span class="keyword">const</span> vec &amp;mu0, <span class="keyword">const</span> sq_T &amp;R0 ) {
-<a name="l00258"></a>00258 <span class="comment">//Fixme test dimensions of mu0 and R0;</span>
-<a name="l00259"></a>00259         mu = mu0;
-<a name="l00260"></a>00260         R = R0;
-<a name="l00261"></a>00261         <span class="keywordflow">if</span> ( _iR.rows() !=R.rows() ) _iR=R; <span class="comment">// memory allocation!</span>
-<a name="l00262"></a>00262         R.inv ( _iR ); <span class="comment">//update cache</span>
-<a name="l00263"></a>00263         cached=<span class="keyword">true</span>;
-<a name="l00264"></a>00264 };
-<a name="l00265"></a>00265 
-<a name="l00266"></a>00266 <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt;
-<a name="l00267"></a>00267 <span class="keywordtype">void</span> <a class="code" href="classenorm.html" title="Gaussian density with positive definite (decomposed) covariance matrix.">enorm&lt;sq_T&gt;::dupdate</a> ( mat &amp;v, <span class="keywordtype">double</span> nu ) {
-<a name="l00268"></a>00268         <span class="comment">//</span>
-<a name="l00269"></a>00269 };
-<a name="l00270"></a>00270 
-<a name="l00271"></a>00271 <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt;
-<a name="l00272"></a><a class="code" href="classenorm.html#5b5fd142b6b17ea334597960e3fe126a">00272</a> <span class="keywordtype">void</span> <a class="code" href="classenorm.html#5b5fd142b6b17ea334597960e3fe126a" title="tupdate in exponential form (not really handy)">enorm&lt;sq_T&gt;::tupdate</a> ( <span class="keywordtype">double</span> phi, mat &amp;vbar, <span class="keywordtype">double</span> nubar ) {
-<a name="l00273"></a>00273         <span class="comment">//</span>
-<a name="l00274"></a>00274 };
-<a name="l00275"></a>00275 
-<a name="l00276"></a>00276 <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt;
-<a name="l00277"></a><a class="code" href="classenorm.html#6020bcd89db2c9584bd8871001bd2023">00277</a> vec <a class="code" href="classenorm.html#6020bcd89db2c9584bd8871001bd2023" title="Returns the required moment of the epdf.">enorm&lt;sq_T&gt;::sample</a>() {
-<a name="l00278"></a>00278         vec x ( <a class="code" href="classenorm.html#6938fc390a19cdaf6ad4503fcbaada4e" title="dimension (redundant from rv.count() for easier coding )">dim</a> );
-<a name="l00279"></a>00279         NorRNG.sample_vector ( <a class="code" href="classenorm.html#6938fc390a19cdaf6ad4503fcbaada4e" title="dimension (redundant from rv.count() for easier coding )">dim</a>,x );
-<a name="l00280"></a>00280         vec smp = <a class="code" href="classenorm.html#4ccc8d8514d644ef1c98d8ab023748a1" title="Covariance matrix in decomposed form.">R</a>.sqrt_mult ( x );
-<a name="l00281"></a>00281 
-<a name="l00282"></a>00282         smp += <a class="code" href="classenorm.html#71fde0d54bba147e00f612577f95ad20" title="mean value">mu</a>;
-<a name="l00283"></a>00283         <span class="keywordflow">return</span> smp;
-<a name="l00284"></a>00284 };
+<a name="l00237"></a>00237         <a class="code" href="classmgamma.html#af43e61b86900c0398d5c0ffc83b94e6" title="Constructor.">mgamma</a> ( <span class="keyword">const</span> <a class="code" href="classRV.html" title="Class representing variables, most often random variables.">RV</a> &amp;<a class="code" href="classmpdf.html#f6687c07ff07d47812dd565368ca59eb" title="modeled random variable">rv</a>,<span class="keyword">const</span> <a class="code" href="classRV.html" title="Class representing variables, most often random variables.">RV</a> &amp;<a class="code" href="classmpdf.html#acb7dda792b3cd5576f39fa3129abbab" title="random variable in condition">rvc</a> );
+<a name="l00239"></a>00239         <span class="keywordtype">void</span> <a class="code" href="classmgamma.html#a9d646cf758a70126dde7c48790b6e94" title="Set value of k.">set_parameters</a> ( <span class="keywordtype">double</span> k );
+<a name="l00241"></a>00241         vec <a class="code" href="classmgamma.html#9f40dc43885085fad8e3d6652b79e139" title="Generate one sample of the posterior.">samplecond</a> ( vec &amp;cond, <span class="keywordtype">double</span> &amp;lik );
+<a name="l00243"></a>00243         mat <a class="code" href="classmgamma.html#9f40dc43885085fad8e3d6652b79e139" title="Generate one sample of the posterior.">samplecond</a> ( vec &amp;cond, vec &amp;lik, <span class="keywordtype">int</span> n );
+<a name="l00244"></a><a class="code" href="classmgamma.html#a61094c9f7a2d64ea77b130cbc031f97">00244</a>         <span class="keywordtype">void</span> <a class="code" href="classmgamma.html#a61094c9f7a2d64ea77b130cbc031f97" title="Update ep so that it represents this mpdf conditioned on rvc = cond.">condition</a> ( <span class="keyword">const</span> vec &amp;val ) {*_beta=k/val;};
+<a name="l00245"></a>00245 };
+<a name="l00246"></a>00246 
+<a name="l00248"></a><a class="code" href="libEF_8h.html#99497a3ff630f761cf6bff7babd23212">00248</a> <span class="keyword">enum</span> <a class="code" href="libEF_8h.html#99497a3ff630f761cf6bff7babd23212" title="Switch between various resampling methods.">RESAMPLING_METHOD</a> { MULTINOMIAL = 0, STRATIFIED = 1, SYSTEMATIC = 3 };
+<a name="l00254"></a><a class="code" href="classeEmp.html">00254</a> <span class="keyword">class </span><a class="code" href="classeEmp.html" title="Weighted empirical density.">eEmp</a>: <span class="keyword">public</span> <a class="code" href="classepdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a> {
+<a name="l00255"></a>00255 <span class="keyword">protected</span> :
+<a name="l00257"></a><a class="code" href="classeEmp.html#8c33034de0e35f03f8bb85d3d67438fd">00257</a>         <span class="keywordtype">int</span> <a class="code" href="classeEmp.html#8c33034de0e35f03f8bb85d3d67438fd" title="Number of particles.">n</a>;
+<a name="l00259"></a><a class="code" href="classeEmp.html#ae78d144404ddba843c93b171b215de8">00259</a>         vec <a class="code" href="classeEmp.html#ae78d144404ddba843c93b171b215de8" title="Sample weights $w$.">w</a>;
+<a name="l00261"></a><a class="code" href="classeEmp.html#a4d6f4bbd6a6824fc39f14676701279a">00261</a>         Array&lt;vec&gt; <a class="code" href="classeEmp.html#a4d6f4bbd6a6824fc39f14676701279a" title="Samples .">samples</a>;
+<a name="l00262"></a>00262 <span class="keyword">public</span>:
+<a name="l00264"></a><a class="code" href="classeEmp.html#0c04b073ecd0dae3d498e680ae27e9e4">00264</a>         <a class="code" href="classeEmp.html#0c04b073ecd0dae3d498e680ae27e9e4" title="Default constructor.">eEmp</a> ( <span class="keyword">const</span> <a class="code" href="classRV.html" title="Class representing variables, most often random variables.">RV</a> &amp;rv0 ,<span class="keywordtype">int</span> n0) :<a class="code" href="classepdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a> ( rv0 ),<a class="code" href="classeEmp.html#8c33034de0e35f03f8bb85d3d67438fd" title="Number of particles.">n</a>(n0),<a class="code" href="classeEmp.html#ae78d144404ddba843c93b171b215de8" title="Sample weights $w$.">w</a>(<a class="code" href="classeEmp.html#8c33034de0e35f03f8bb85d3d67438fd" title="Number of particles.">n</a>),<a class="code" href="classeEmp.html#a4d6f4bbd6a6824fc39f14676701279a" title="Samples .">samples</a>(<a class="code" href="classeEmp.html#8c33034de0e35f03f8bb85d3d67438fd" title="Number of particles.">n</a>) {};
+<a name="l00266"></a>00266         <span class="keywordtype">void</span> <a class="code" href="classeEmp.html#6606a656c1b28114f7384c25aaf80e8d" title="Set sample.">set_parameters</a> ( <span class="keyword">const</span> vec &amp;w0, <a class="code" href="classepdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a>* pdf0 );
+<a name="l00268"></a><a class="code" href="classeEmp.html#31b2bfb73b72486a5c89f2ab850c7a9b">00268</a>         vec&amp; <a class="code" href="classeEmp.html#31b2bfb73b72486a5c89f2ab850c7a9b" title="Potentially dangerous, use with care.">_w</a>()  {<span class="keywordflow">return</span> <a class="code" href="classeEmp.html#ae78d144404ddba843c93b171b215de8" title="Sample weights $w$.">w</a>;};
+<a name="l00270"></a><a class="code" href="classeEmp.html#31b747eca73b16f30370827ba4cc3575">00270</a>         Array&lt;vec&gt;&amp; <a class="code" href="classeEmp.html#31b747eca73b16f30370827ba4cc3575" title="access function">_samples</a>() {<span class="keywordflow">return</span> <a class="code" href="classeEmp.html#a4d6f4bbd6a6824fc39f14676701279a" title="Samples .">samples</a>;};
+<a name="l00272"></a>00272         ivec <a class="code" href="classeEmp.html#77268292fc4465cb73ddbfb1f2932a59" title="Function performs resampling, i.e. removal of low-weight samples and duplication...">resample</a> ( <a class="code" href="libEF_8h.html#99497a3ff630f761cf6bff7babd23212" title="Switch between various resampling methods.">RESAMPLING_METHOD</a> method = SYSTEMATIC );
+<a name="l00274"></a><a class="code" href="classeEmp.html#83f9283f92b805508d896479dc1ccf12">00274</a>         vec <a class="code" href="classeEmp.html#83f9283f92b805508d896479dc1ccf12" title="inherited operation : NOT implemneted">sample</a>()<span class="keyword"> const </span>{it_error ( <span class="stringliteral">"Not implemented"</span> );<span class="keywordflow">return</span> 0;}
+<a name="l00276"></a><a class="code" href="classeEmp.html#23e7358995400865ad2e278945922fb3">00276</a>         <span class="keywordtype">double</span> <a class="code" href="classeEmp.html#23e7358995400865ad2e278945922fb3" title="inherited operation : NOT implemneted">evalpdflog</a>(<span class="keyword">const</span> vec &amp;val)<span class="keyword"> const </span>{it_error ( <span class="stringliteral">"Not implemented"</span> );<span class="keywordflow">return</span> 0.0;}
+<a name="l00277"></a><a class="code" href="classeEmp.html#ba055c19038cc72628d98e25197e982d">00277</a>         vec <a class="code" href="classeEmp.html#ba055c19038cc72628d98e25197e982d" title="return expected value">mean</a>()<span class="keyword">const </span>{vec pom=zeros(<a class="code" href="classepdf.html#74da992e3f5d598da8850b646b79b9d9" title="Identified of the random variable.">rv</a>.<a class="code" href="classRV.html#f5c7b8bd589eef09ccdf3329a0addea0" title="Return length (number of scalars) of the RV.">count</a>()); 
+<a name="l00278"></a>00278                 <span class="keywordflow">for</span> (<span class="keywordtype">int</span> i=0;i&lt;<a class="code" href="classeEmp.html#8c33034de0e35f03f8bb85d3d67438fd" title="Number of particles.">n</a>;i++){pom+=<a class="code" href="classeEmp.html#a4d6f4bbd6a6824fc39f14676701279a" title="Samples .">samples</a>(i)*<a class="code" href="classeEmp.html#ae78d144404ddba843c93b171b215de8" title="Sample weights $w$.">w</a>(i);}
+<a name="l00279"></a>00279                 <span class="keywordflow">return</span> pom;
+<a name="l00280"></a>00280         }
+<a name="l00281"></a>00281 };
+<a name="l00282"></a>00282 
+<a name="l00283"></a>00283 
 <a name="l00285"></a>00285 
 <a name="l00286"></a>00286 <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt;
-<a name="l00287"></a>00287 mat <a class="code" href="classenorm.html#6020bcd89db2c9584bd8871001bd2023" title="Returns the required moment of the epdf.">enorm&lt;sq_T&gt;::sample</a> ( <span class="keywordtype">int</span> N ) {
-<a name="l00288"></a>00288         mat X ( <a class="code" href="classenorm.html#6938fc390a19cdaf6ad4503fcbaada4e" title="dimension (redundant from rv.count() for easier coding )">dim</a>,N );
-<a name="l00289"></a>00289         vec x ( <a class="code" href="classenorm.html#6938fc390a19cdaf6ad4503fcbaada4e" title="dimension (redundant from rv.count() for easier coding )">dim</a> );
-<a name="l00290"></a>00290         vec pom;
-<a name="l00291"></a>00291         <span class="keywordtype">int</span> i;
-<a name="l00292"></a>00292 
-<a name="l00293"></a>00293         <span class="keywordflow">for</span> ( i=0;i&lt;N;i++ ) {
-<a name="l00294"></a>00294                 NorRNG.sample_vector ( <a class="code" href="classenorm.html#6938fc390a19cdaf6ad4503fcbaada4e" title="dimension (redundant from rv.count() for easier coding )">dim</a>,x );
-<a name="l00295"></a>00295                 pom = <a class="code" href="classenorm.html#4ccc8d8514d644ef1c98d8ab023748a1" title="Covariance matrix in decomposed form.">R</a>.sqrt_mult ( x );
-<a name="l00296"></a>00296                 pom +=<a class="code" href="classenorm.html#71fde0d54bba147e00f612577f95ad20" title="mean value">mu</a>;
-<a name="l00297"></a>00297                 X.set_col ( i, pom );
-<a name="l00298"></a>00298         }
-<a name="l00299"></a>00299 
-<a name="l00300"></a>00300         <span class="keywordflow">return</span> X;
-<a name="l00301"></a>00301 };
-<a name="l00302"></a>00302 
-<a name="l00303"></a>00303 <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt;
-<a name="l00304"></a><a class="code" href="classenorm.html#93107f05a8e9b34b64853767200121a4">00304</a> <span class="keywordtype">double</span> <a class="code" href="classenorm.html#93107f05a8e9b34b64853767200121a4" title="Compute probability of argument val.">enorm&lt;sq_T&gt;::eval</a> ( <span class="keyword">const</span> vec &amp;val ) {
-<a name="l00305"></a>00305         <span class="keywordtype">double</span> pdfl,e;
-<a name="l00306"></a>00306         pdfl = <a class="code" href="classenorm.html#9517594915e897584eaebbb057ed8881" title="Compute log-probability of argument val.">evalpdflog</a> ( val );
-<a name="l00307"></a>00307         e = exp ( pdfl );
-<a name="l00308"></a>00308         <span class="keywordflow">return</span> e;
-<a name="l00309"></a>00309 };
-<a name="l00310"></a>00310 
-<a name="l00311"></a>00311 <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt;
-<a name="l00312"></a><a class="code" href="classenorm.html#9517594915e897584eaebbb057ed8881">00312</a> <span class="keywordtype">double</span> <a class="code" href="classenorm.html#9517594915e897584eaebbb057ed8881" title="Compute log-probability of argument val.">enorm&lt;sq_T&gt;::evalpdflog</a> ( <span class="keyword">const</span> vec &amp;val ) {
-<a name="l00313"></a>00313         <span class="keywordflow">if</span> ( !<a class="code" href="classenorm.html#ae12db77283a96e0f14a3eae93dc3bf1" title="indicator if _iR is chached">cached</a> ) {<a class="code" href="classenorm.html#4ccc8d8514d644ef1c98d8ab023748a1" title="Covariance matrix in decomposed form.">R</a>.inv ( <a class="code" href="classenorm.html#82f39ac49911d7097f4bfe385deba355" title="Cache: _iR = inv(R);.">_iR</a> );<a class="code" href="classenorm.html#ae12db77283a96e0f14a3eae93dc3bf1" title="indicator if _iR is chached">cached</a>=<span class="keyword">true</span>;}
+<a name="l00287"></a><a class="code" href="classenorm.html#7b5cb487a2570e8109bfdc0df149aa06">00287</a> <a class="code" href="classenorm.html#7b5cb487a2570e8109bfdc0df149aa06" title="Default constructor.">enorm&lt;sq_T&gt;::enorm</a> ( <a class="code" href="classRV.html" title="Class representing variables, most often random variables.">RV</a> &amp;rv ) :<a class="code" href="classeEF.html" title="General conjugate exponential family posterior density.">eEF</a>(rv), mu ( rv.count() ),R ( rv.count() ),_iR ( rv.count() ),cached ( false ),dim ( rv.count() ) {};
+<a name="l00288"></a>00288 
+<a name="l00289"></a>00289 <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt;
+<a name="l00290"></a><a class="code" href="classenorm.html#1394a65caa6e00d42e00cc99b12227af">00290</a> <span class="keywordtype">void</span> <a class="code" href="classenorm.html#1394a65caa6e00d42e00cc99b12227af" title="Set mean value mu and covariance R.">enorm&lt;sq_T&gt;::set_parameters</a> ( <span class="keyword">const</span> vec &amp;mu0, <span class="keyword">const</span> sq_T &amp;R0 ) {
+<a name="l00291"></a>00291 <span class="comment">//Fixme test dimensions of mu0 and R0;</span>
+<a name="l00292"></a>00292         <a class="code" href="classenorm.html#71fde0d54bba147e00f612577f95ad20" title="mean value">mu</a> = mu0;
+<a name="l00293"></a>00293         <a class="code" href="classenorm.html#4ccc8d8514d644ef1c98d8ab023748a1" title="Covariance matrix in decomposed form.">R</a> = R0;
+<a name="l00294"></a>00294         <span class="keywordflow">if</span> ( <a class="code" href="classenorm.html#82f39ac49911d7097f4bfe385deba355" title="Cache: _iR = inv(R);.">_iR</a>.rows() !=<a class="code" href="classenorm.html#4ccc8d8514d644ef1c98d8ab023748a1" title="Covariance matrix in decomposed form.">R</a>.rows() ) <a class="code" href="classenorm.html#82f39ac49911d7097f4bfe385deba355" title="Cache: _iR = inv(R);.">_iR</a>=<a class="code" href="classenorm.html#4ccc8d8514d644ef1c98d8ab023748a1" title="Covariance matrix in decomposed form.">R</a>; <span class="comment">// memory allocation!</span>
+<a name="l00295"></a>00295         <a class="code" href="classenorm.html#4ccc8d8514d644ef1c98d8ab023748a1" title="Covariance matrix in decomposed form.">R</a>.inv ( <a class="code" href="classenorm.html#82f39ac49911d7097f4bfe385deba355" title="Cache: _iR = inv(R);.">_iR</a> ); <span class="comment">//update cache</span>
+<a name="l00296"></a>00296         <a class="code" href="classenorm.html#ae12db77283a96e0f14a3eae93dc3bf1" title="indicator if _iR is chached">cached</a>=<span class="keyword">true</span>;
+<a name="l00297"></a>00297 };
+<a name="l00298"></a>00298 
+<a name="l00299"></a>00299 <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt;
+<a name="l00300"></a><a class="code" href="classenorm.html#5bf185e31e5954fceb90ada3debd2ff2">00300</a> <span class="keywordtype">void</span> <a class="code" href="classenorm.html#5bf185e31e5954fceb90ada3debd2ff2" title="dupdate in exponential form (not really handy)">enorm&lt;sq_T&gt;::dupdate</a> ( mat &amp;v, <span class="keywordtype">double</span> nu ) {
+<a name="l00301"></a>00301         <span class="comment">//</span>
+<a name="l00302"></a>00302 };
+<a name="l00303"></a>00303 
+<a name="l00304"></a>00304 <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt;
+<a name="l00305"></a><a class="code" href="classenorm.html#5b5fd142b6b17ea334597960e3fe126a">00305</a> <span class="keywordtype">void</span> <a class="code" href="classenorm.html#5b5fd142b6b17ea334597960e3fe126a" title="tupdate in exponential form (not really handy)">enorm&lt;sq_T&gt;::tupdate</a> ( <span class="keywordtype">double</span> phi, mat &amp;vbar, <span class="keywordtype">double</span> nubar ) {
+<a name="l00306"></a>00306         <span class="comment">//</span>
+<a name="l00307"></a>00307 };
+<a name="l00308"></a>00308 
+<a name="l00309"></a>00309 <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt;
+<a name="l00310"></a><a class="code" href="classenorm.html#60b47544f6181ffd4530d3e415ce12c5">00310</a> vec <a class="code" href="classenorm.html#60b47544f6181ffd4530d3e415ce12c5" title="Returns the required moment of the epdf.">enorm&lt;sq_T&gt;::sample</a>()<span class="keyword"> const </span>{
+<a name="l00311"></a>00311         vec x ( <a class="code" href="classenorm.html#6938fc390a19cdaf6ad4503fcbaada4e" title="dimension (redundant from rv.count() for easier coding )">dim</a> );
+<a name="l00312"></a>00312         NorRNG.sample_vector ( <a class="code" href="classenorm.html#6938fc390a19cdaf6ad4503fcbaada4e" title="dimension (redundant from rv.count() for easier coding )">dim</a>,x );
+<a name="l00313"></a>00313         vec smp = <a class="code" href="classenorm.html#4ccc8d8514d644ef1c98d8ab023748a1" title="Covariance matrix in decomposed form.">R</a>.sqrt_mult ( x );
 <a name="l00314"></a>00314 
-<a name="l00315"></a>00315         <span class="keywordflow">return</span> -0.5* ( <a class="code" href="classenorm.html#4ccc8d8514d644ef1c98d8ab023748a1" title="Covariance matrix in decomposed form.">R</a>.cols() *0.79817986835811504957 +<a class="code" href="classenorm.html#4ccc8d8514d644ef1c98d8ab023748a1" title="Covariance matrix in decomposed form.">R</a>.logdet() +<a class="code" href="classenorm.html#82f39ac49911d7097f4bfe385deba355" title="Cache: _iR = inv(R);.">_iR</a>.qform ( <a class="code" href="classenorm.html#71fde0d54bba147e00f612577f95ad20" title="mean value">mu</a>-val ) );
-<a name="l00316"></a>00316 };
-<a name="l00317"></a>00317 
+<a name="l00315"></a>00315         smp += <a class="code" href="classenorm.html#71fde0d54bba147e00f612577f95ad20" title="mean value">mu</a>;
+<a name="l00316"></a>00316         <span class="keywordflow">return</span> smp;
+<a name="l00317"></a>00317 };
 <a name="l00318"></a>00318 
 <a name="l00319"></a>00319 <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt;
-<a name="l00320"></a><a class="code" href="classmlnorm.html#f927203b3f31171c5c10ffc7caa797f5">00320</a> <a class="code" href="classmlnorm.html#f927203b3f31171c5c10ffc7caa797f5" title="Constructor.">mlnorm&lt;sq_T&gt;::mlnorm</a> ( <a class="code" href="classRV.html" title="Class representing variables, most often random variables.">RV</a> &amp;rv0,<a class="code" href="classRV.html" title="Class representing variables, most often random variables.">RV</a> &amp;rvc0 ) :mEF ( rv0,rvc0 ),<a class="code" href="classepdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a> ( rv ),A ( rv0.count(),rv0.count() ) {
-<a name="l00321"></a>00321         _mu = <a class="code" href="classepdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a>._mu();
-<a name="l00322"></a>00322 }
-<a name="l00323"></a>00323 
-<a name="l00324"></a>00324 <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt;
-<a name="l00325"></a>00325 <span class="keywordtype">void</span> <a class="code" href="classmlnorm.html" title="Normal distributed linear function with linear function of mean value;.">mlnorm&lt;sq_T&gt;::set_parameters</a> ( <span class="keyword">const</span> mat &amp;A0, <span class="keyword">const</span> sq_T &amp;R0 ) {
-<a name="l00326"></a>00326         <a class="code" href="classepdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a>.set_parameters ( zeros ( rv.count() ),R0 );
-<a name="l00327"></a>00327         A = A0;
-<a name="l00328"></a>00328 }
-<a name="l00329"></a>00329 
-<a name="l00330"></a>00330 <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt;
-<a name="l00331"></a><a class="code" href="classmlnorm.html#decf3e3b5c8e0812e5b4dbe94fa2ae18">00331</a> vec <a class="code" href="classmlnorm.html#decf3e3b5c8e0812e5b4dbe94fa2ae18" title="Generate one sample of the posterior.">mlnorm&lt;sq_T&gt;::samplecond</a> ( vec &amp;cond, <span class="keywordtype">double</span> &amp;lik ) {
-<a name="l00332"></a>00332         this-&gt;condition ( cond );
-<a name="l00333"></a>00333         vec smp = <a class="code" href="classepdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a>.sample();
-<a name="l00334"></a>00334         lik = <a class="code" href="classepdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a>.eval ( smp );
-<a name="l00335"></a>00335         <span class="keywordflow">return</span> smp;
-<a name="l00336"></a>00336 }
-<a name="l00337"></a>00337 
-<a name="l00338"></a>00338 <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt;
-<a name="l00339"></a><a class="code" href="classmlnorm.html#215fb88cc8b95d64cdefd6849abdd1e8">00339</a> mat <a class="code" href="classmlnorm.html#decf3e3b5c8e0812e5b4dbe94fa2ae18" title="Generate one sample of the posterior.">mlnorm&lt;sq_T&gt;::samplecond</a> ( vec &amp;cond, vec &amp;lik, <span class="keywordtype">int</span> n ) {
-<a name="l00340"></a>00340         <span class="keywordtype">int</span> i;
-<a name="l00341"></a>00341         <span class="keywordtype">int</span> dim = rv.count();
-<a name="l00342"></a>00342         mat Smp ( dim,n );
-<a name="l00343"></a>00343         vec smp ( dim );
-<a name="l00344"></a>00344         this-&gt;condition ( cond );
-<a name="l00345"></a>00345 
-<a name="l00346"></a>00346         <span class="keywordflow">for</span> ( i=0; i&lt;n; i++ ) {
-<a name="l00347"></a>00347                 smp = <a class="code" href="classepdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a>.sample();
-<a name="l00348"></a>00348                 lik ( i ) = <a class="code" href="classepdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a>.eval ( smp );
-<a name="l00349"></a>00349                 Smp.set_col ( i ,smp );
-<a name="l00350"></a>00350         }
+<a name="l00320"></a><a class="code" href="classenorm.html#60f0f3bfa53d6e65843eea9532b16d36">00320</a> mat <a class="code" href="classenorm.html#60b47544f6181ffd4530d3e415ce12c5" title="Returns the required moment of the epdf.">enorm&lt;sq_T&gt;::sample</a> ( <span class="keywordtype">int</span> N )<span class="keyword">const </span>{
+<a name="l00321"></a>00321         mat X ( <a class="code" href="classenorm.html#6938fc390a19cdaf6ad4503fcbaada4e" title="dimension (redundant from rv.count() for easier coding )">dim</a>,N );
+<a name="l00322"></a>00322         vec x ( <a class="code" href="classenorm.html#6938fc390a19cdaf6ad4503fcbaada4e" title="dimension (redundant from rv.count() for easier coding )">dim</a> );
+<a name="l00323"></a>00323         vec pom;
+<a name="l00324"></a>00324         <span class="keywordtype">int</span> i;
+<a name="l00325"></a>00325 
+<a name="l00326"></a>00326         <span class="keywordflow">for</span> ( i=0;i&lt;N;i++ ) {
+<a name="l00327"></a>00327                 NorRNG.sample_vector ( <a class="code" href="classenorm.html#6938fc390a19cdaf6ad4503fcbaada4e" title="dimension (redundant from rv.count() for easier coding )">dim</a>,x );
+<a name="l00328"></a>00328                 pom = <a class="code" href="classenorm.html#4ccc8d8514d644ef1c98d8ab023748a1" title="Covariance matrix in decomposed form.">R</a>.sqrt_mult ( x );
+<a name="l00329"></a>00329                 pom +=<a class="code" href="classenorm.html#71fde0d54bba147e00f612577f95ad20" title="mean value">mu</a>;
+<a name="l00330"></a>00330                 X.set_col ( i, pom );
+<a name="l00331"></a>00331         }
+<a name="l00332"></a>00332 
+<a name="l00333"></a>00333         <span class="keywordflow">return</span> X;
+<a name="l00334"></a>00334 };
+<a name="l00335"></a>00335 
+<a name="l00336"></a>00336 <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt;
+<a name="l00337"></a><a class="code" href="classenorm.html#b9e1dfd33692d7b3f1a59f17b0e61bd0">00337</a> <span class="keywordtype">double</span> <a class="code" href="classenorm.html#b9e1dfd33692d7b3f1a59f17b0e61bd0" title="Compute probability of argument val.">enorm&lt;sq_T&gt;::eval</a> ( <span class="keyword">const</span> vec &amp;val )<span class="keyword"> const </span>{
+<a name="l00338"></a>00338         <span class="keywordtype">double</span> pdfl,e;
+<a name="l00339"></a>00339         pdfl = <a class="code" href="classenorm.html#609a7c33dbb4fdfab050f3bdd1122401" title="Compute log-probability of argument val.">evalpdflog</a> ( val );
+<a name="l00340"></a>00340         e = exp ( pdfl );
+<a name="l00341"></a>00341         <span class="keywordflow">return</span> e;
+<a name="l00342"></a>00342 };
+<a name="l00343"></a>00343 
+<a name="l00344"></a>00344 <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt;
+<a name="l00345"></a><a class="code" href="classenorm.html#609a7c33dbb4fdfab050f3bdd1122401">00345</a> <span class="keywordtype">double</span> <a class="code" href="classenorm.html#609a7c33dbb4fdfab050f3bdd1122401" title="Compute log-probability of argument val.">enorm&lt;sq_T&gt;::evalpdflog</a> ( <span class="keyword">const</span> vec &amp;val )<span class="keyword"> const </span>{
+<a name="l00346"></a>00346         <span class="keywordflow">if</span> ( !<a class="code" href="classenorm.html#ae12db77283a96e0f14a3eae93dc3bf1" title="indicator if _iR is chached">cached</a> ) {it_error(<span class="stringliteral">"this should not happen, see cached"</span>);}
+<a name="l00347"></a>00347 
+<a name="l00348"></a>00348         <span class="comment">// 1.83787706640935 = log(2pi)</span>
+<a name="l00349"></a>00349         <span class="keywordflow">return</span> -0.5* ( <a class="code" href="classenorm.html#4ccc8d8514d644ef1c98d8ab023748a1" title="Covariance matrix in decomposed form.">R</a>.cols() * 1.83787706640935 +<a class="code" href="classenorm.html#4ccc8d8514d644ef1c98d8ab023748a1" title="Covariance matrix in decomposed form.">R</a>.logdet() +<a class="code" href="classenorm.html#82f39ac49911d7097f4bfe385deba355" title="Cache: _iR = inv(R);.">_iR</a>.qform ( <a class="code" href="classenorm.html#71fde0d54bba147e00f612577f95ad20" title="mean value">mu</a>-val ) );
+<a name="l00350"></a>00350 };
 <a name="l00351"></a>00351 
-<a name="l00352"></a>00352         <span class="keywordflow">return</span> Smp;
-<a name="l00353"></a>00353 }
-<a name="l00354"></a>00354 
-<a name="l00355"></a>00355 <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt;
-<a name="l00356"></a>00356 <span class="keywordtype">void</span> <a class="code" href="classmlnorm.html" title="Normal distributed linear function with linear function of mean value;.">mlnorm&lt;sq_T&gt;::condition</a> ( vec &amp;cond ) {
-<a name="l00357"></a>00357         *_mu = A*cond;
-<a name="l00358"></a>00358 <span class="comment">//R is already assigned;</span>
-<a name="l00359"></a>00359 }
-<a name="l00360"></a>00360 
-<a name="l00362"></a>00362 
+<a name="l00352"></a>00352 
+<a name="l00353"></a>00353 <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt;
+<a name="l00354"></a><a class="code" href="classmlnorm.html#f927203b3f31171c5c10ffc7caa797f5">00354</a> <a class="code" href="classmlnorm.html#f927203b3f31171c5c10ffc7caa797f5" title="Constructor.">mlnorm&lt;sq_T&gt;::mlnorm</a> ( <a class="code" href="classRV.html" title="Class representing variables, most often random variables.">RV</a> &amp;rv0,<a class="code" href="classRV.html" title="Class representing variables, most often random variables.">RV</a> &amp;rvc0 ) :<a class="code" href="classmEF.html" title="Exponential family model.">mEF</a> ( rv0,rvc0 ),<a class="code" href="classepdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a> ( <a class="code" href="classepdf.html#74da992e3f5d598da8850b646b79b9d9" title="Identified of the random variable.">rv</a> ),A ( rv0.count(),rv0.count() ) {
+<a name="l00355"></a>00355         _mu = <a class="code" href="classepdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a>._mu();
+<a name="l00356"></a>00356 }
+<a name="l00357"></a>00357 
+<a name="l00358"></a>00358 <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt;
+<a name="l00359"></a><a class="code" href="classmlnorm.html#b6749030c5d5abcb3eb6898f74cea3c0">00359</a> <span class="keywordtype">void</span> <a class="code" href="classmlnorm.html#b6749030c5d5abcb3eb6898f74cea3c0" title="Set A and R.">mlnorm&lt;sq_T&gt;::set_parameters</a> ( <span class="keyword">const</span> mat &amp;A0, <span class="keyword">const</span> sq_T &amp;R0 ) {
+<a name="l00360"></a>00360         <a class="code" href="classepdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a>.set_parameters ( zeros ( <a class="code" href="classmpdf.html#f6687c07ff07d47812dd565368ca59eb" title="modeled random variable">rv</a>.<a class="code" href="classRV.html#f5c7b8bd589eef09ccdf3329a0addea0" title="Return length (number of scalars) of the RV.">count</a>() ),R0 );
+<a name="l00361"></a>00361         A = A0;
+<a name="l00362"></a>00362 }
 <a name="l00363"></a>00363 
-<a name="l00364"></a>00364 <span class="preprocessor">#endif //EF_H</span>
-</pre></div><hr size="1"><address style="text-align: right;"><small>Generated on Thu Feb 28 16:54:40 2008 for mixpp by&nbsp;
+<a name="l00364"></a>00364 <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt;
+<a name="l00365"></a><a class="code" href="classmlnorm.html#decf3e3b5c8e0812e5b4dbe94fa2ae18">00365</a> vec <a class="code" href="classmlnorm.html#decf3e3b5c8e0812e5b4dbe94fa2ae18" title="Generate one sample of the posterior.">mlnorm&lt;sq_T&gt;::samplecond</a> ( vec &amp;cond, <span class="keywordtype">double</span> &amp;lik ) {
+<a name="l00366"></a>00366         this-&gt;<a class="code" href="classmlnorm.html#5232fc7e305eceab4e2bd6a8daa44195" title="Set value of rvc . Result of this operation is stored in epdf use function _ep to...">condition</a> ( cond );
+<a name="l00367"></a>00367         vec smp = <a class="code" href="classepdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a>.sample();
+<a name="l00368"></a>00368         lik = <a class="code" href="classepdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a>.eval ( smp );
+<a name="l00369"></a>00369         <span class="keywordflow">return</span> smp;
+<a name="l00370"></a>00370 }
+<a name="l00371"></a>00371 
+<a name="l00372"></a>00372 <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt;
+<a name="l00373"></a><a class="code" href="classmlnorm.html#215fb88cc8b95d64cdefd6849abdd1e8">00373</a> mat <a class="code" href="classmlnorm.html#decf3e3b5c8e0812e5b4dbe94fa2ae18" title="Generate one sample of the posterior.">mlnorm&lt;sq_T&gt;::samplecond</a> ( vec &amp;cond, vec &amp;lik, <span class="keywordtype">int</span> n ) {
+<a name="l00374"></a>00374         <span class="keywordtype">int</span> i;
+<a name="l00375"></a>00375         <span class="keywordtype">int</span> dim = <a class="code" href="classmpdf.html#f6687c07ff07d47812dd565368ca59eb" title="modeled random variable">rv</a>.<a class="code" href="classRV.html#f5c7b8bd589eef09ccdf3329a0addea0" title="Return length (number of scalars) of the RV.">count</a>();
+<a name="l00376"></a>00376         mat Smp ( dim,n );
+<a name="l00377"></a>00377         vec smp ( dim );
+<a name="l00378"></a>00378         this-&gt;<a class="code" href="classmlnorm.html#5232fc7e305eceab4e2bd6a8daa44195" title="Set value of rvc . Result of this operation is stored in epdf use function _ep to...">condition</a> ( cond );
+<a name="l00379"></a>00379 
+<a name="l00380"></a>00380         <span class="keywordflow">for</span> ( i=0; i&lt;n; i++ ) {
+<a name="l00381"></a>00381                 smp = <a class="code" href="classepdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a>.sample();
+<a name="l00382"></a>00382                 lik ( i ) = <a class="code" href="classepdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a>.eval ( smp );
+<a name="l00383"></a>00383                 Smp.set_col ( i ,smp );
+<a name="l00384"></a>00384         }
+<a name="l00385"></a>00385 
+<a name="l00386"></a>00386         <span class="keywordflow">return</span> Smp;
+<a name="l00387"></a>00387 }
+<a name="l00388"></a>00388 
+<a name="l00389"></a>00389 <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt;
+<a name="l00390"></a><a class="code" href="classmlnorm.html#5232fc7e305eceab4e2bd6a8daa44195">00390</a> <span class="keywordtype">void</span> <a class="code" href="classmlnorm.html#5232fc7e305eceab4e2bd6a8daa44195" title="Set value of rvc . Result of this operation is stored in epdf use function _ep to...">mlnorm&lt;sq_T&gt;::condition</a> ( vec &amp;cond ) {
+<a name="l00391"></a>00391         *_mu = A*cond;
+<a name="l00392"></a>00392 <span class="comment">//R is already assigned;</span>
+<a name="l00393"></a>00393 }
+<a name="l00394"></a>00394 
+<a name="l00396"></a>00396 
+<a name="l00397"></a>00397 
+<a name="l00398"></a>00398 <span class="preprocessor">#endif //EF_H</span>
+</pre></div><hr size="1"><address style="text-align: right;"><small>Generated on Wed Mar 5 15:40:01 2008 for mixpp by&nbsp;
 <a href="http://www.doxygen.org/index.html">
 <img src="doxygen.png" alt="doxygen" align="middle" border="0"></a> 1.5.3 </small></address>