\section{mlnorm$<$ sq\_\-T $>$ Class Template Reference}
\label{classmlnorm}\index{mlnorm@{mlnorm}}
Normal distributed linear function with linear function of mean value;.  


{\tt \#include $<$libEF.h$>$}

Inheritance diagram for mlnorm$<$ sq\_\-T $>$:\nopagebreak
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\subsection*{Public Member Functions}
\begin{CompactItemize}
\item 
{\bf mlnorm} ({\bf RV} \&{\bf rv}, {\bf RV} \&{\bf rvc})\label{classmlnorm_f927203b3f31171c5c10ffc7caa797f5}

\begin{CompactList}\small\item\em Constructor. \item\end{CompactList}\item 
void {\bf set\_\-parameters} (const mat \&A, const sq\_\-T \&R)\label{classmlnorm_b6749030c5d5abcb3eb6898f74cea3c0}

\begin{CompactList}\small\item\em Set {\tt A} and {\tt R}. \item\end{CompactList}\item 
vec {\bf samplecond} (vec \&cond, double \&lik)\label{classmlnorm_decf3e3b5c8e0812e5b4dbe94fa2ae18}

\begin{CompactList}\small\item\em Generate one sample of the posterior. \item\end{CompactList}\item 
mat {\bf samplecond} (vec \&cond, vec \&lik, int n)\label{classmlnorm_215fb88cc8b95d64cdefd6849abdd1e8}

\begin{CompactList}\small\item\em Generate matrix of samples of the posterior. \item\end{CompactList}\item 
void {\bf condition} (vec \&cond)\label{classmlnorm_5232fc7e305eceab4e2bd6a8daa44195}

\begin{CompactList}\small\item\em Set value of {\tt rvc} . Result of this operation is stored in {\tt \doxyref{epdf}{p.}{classepdf}} use function {\tt \_\-ep} to access it. \item\end{CompactList}\item 
virtual vec {\bf samplecond} (const vec \&cond, double \&ll)
\begin{CompactList}\small\item\em Returns the required moment of the \doxyref{epdf}{p.}{classepdf}. \item\end{CompactList}\item 
virtual mat {\bf samplecond} (const vec \&cond, vec \&ll, int N)
\begin{CompactList}\small\item\em Returns. \item\end{CompactList}\item 
virtual void {\bf condition} (const vec \&cond)\label{classmpdf_0f95a0cc6ab40611f46804682446ed83}

\begin{CompactList}\small\item\em Update {\tt ep} so that it represents this \doxyref{mpdf}{p.}{classmpdf} conditioned on {\tt rvc} = cond. \item\end{CompactList}\item 
virtual double {\bf evalcond} (const vec \&dt, const vec \&cond)\label{classmpdf_80b738ece5bd4f8c4edaee4b38906f91}

\begin{CompactList}\small\item\em Shortcut for conditioning and evaluation of the internal \doxyref{epdf}{p.}{classepdf}. In some cases, this operation can be implemented efficiently. \item\end{CompactList}\item 
{\bf RV} {\bf \_\-rvc} ()\label{classmpdf_ec9c850305984582548e8deb64f0ffe8}

\begin{CompactList}\small\item\em access function \item\end{CompactList}\item 
{\bf RV} {\bf \_\-rv} ()\label{classmpdf_1e71ad4c66d5884c82d4a3b06b42fe32}

\begin{CompactList}\small\item\em access function \item\end{CompactList}\item 
{\bf epdf} \& {\bf \_\-epdf} ()\label{classmpdf_e17780ee5b2cfe05922a6c56af1462f8}

\begin{CompactList}\small\item\em access function \item\end{CompactList}\end{CompactItemize}
\subsection*{Protected Attributes}
\begin{CompactItemize}
\item 
{\bf RV} {\bf rv}\label{classmpdf_f6687c07ff07d47812dd565368ca59eb}

\begin{CompactList}\small\item\em modeled random variable \item\end{CompactList}\item 
{\bf RV} {\bf rvc}\label{classmpdf_acb7dda792b3cd5576f39fa3129abbab}

\begin{CompactList}\small\item\em random variable in condition \item\end{CompactList}\item 
{\bf epdf} $\ast$ {\bf ep}\label{classmpdf_7aa894208a32f3487827df6d5054424c}

\begin{CompactList}\small\item\em pointer to internal \doxyref{epdf}{p.}{classepdf} \item\end{CompactList}\end{CompactItemize}


\subsection{Detailed Description}
\subsubsection*{template$<$class sq\_\-T$>$ class mlnorm$<$ sq\_\-T $>$}

Normal distributed linear function with linear function of mean value;. 

Mean value $mu=A*rvc$. 

\subsection{Member Function Documentation}
\index{mlnorm@{mlnorm}!samplecond@{samplecond}}
\index{samplecond@{samplecond}!mlnorm@{mlnorm}}
\subsubsection[samplecond]{\setlength{\rightskip}{0pt plus 5cm}virtual vec mpdf::samplecond (const vec \& {\em cond}, \/  double \& {\em ll})\hspace{0.3cm}{\tt  [inline, virtual, inherited]}}\label{classmpdf_3f172b79ec4a5ebc87898a5381141f1b}


Returns the required moment of the \doxyref{epdf}{p.}{classepdf}. 

Returns a sample from the density conditioned on {\tt cond}, $x \sim epdf(rv|cond)$. \begin{Desc}
\item[Parameters:]
\begin{description}
\item[{\em cond}]is numeric value of {\tt rv} \item[{\em ll}]is a return value of log-likelihood of the sample. \end{description}
\end{Desc}


References mpdf::condition(), mpdf::ep, epdf::evalpdflog(), and epdf::sample().

Referenced by MPF$<$ BM\_\-T $>$::bayes(), and PF::bayes().\index{mlnorm@{mlnorm}!samplecond@{samplecond}}
\index{samplecond@{samplecond}!mlnorm@{mlnorm}}
\subsubsection[samplecond]{\setlength{\rightskip}{0pt plus 5cm}virtual mat mpdf::samplecond (const vec \& {\em cond}, \/  vec \& {\em ll}, \/  int {\em N})\hspace{0.3cm}{\tt  [inline, virtual, inherited]}}\label{classmpdf_0e37163660f93df2a4d723cedb1da89c}


Returns. 

\begin{Desc}
\item[Parameters:]
\begin{description}
\item[{\em N}]samples from the density conditioned on {\tt cond}, $x \sim epdf(rv|cond)$. \item[{\em cond}]is numeric value of {\tt rv} \item[{\em ll}]is a return value of log-likelihood of the sample. \end{description}
\end{Desc}


References mpdf::condition(), RV::count(), mpdf::ep, epdf::evalpdflog(), mpdf::rv, and epdf::sample().

The documentation for this class was generated from the following file:\begin{CompactItemize}
\item 
work/git/mixpp/bdm/stat/{\bf libEF.h}\end{CompactItemize}
