\section{work/mixpp/bdm/stat/libBM.h File Reference}
\label{libBM_8h}\index{work/mixpp/bdm/stat/libBM.h@{work/mixpp/bdm/stat/libBM.h}}
Bayesian Models (bm) that use Bayes rule to learn from observations. 

{\tt \#include $<$itpp/itbase.h$>$}\par


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\subsection*{Classes}
\begin{CompactItemize}
\item 
class {\bf RV}
\begin{CompactList}\small\item\em Class representing variables, most often random variables. \item\end{CompactList}\item 
class {\bf fnc}
\begin{CompactList}\small\item\em Class representing function \$f(x)\$ of variable \$x\$ represented by {\tt rv}. \item\end{CompactList}\item 
class {\bf BM}
\begin{CompactList}\small\item\em Bayesian Model of the world, i.e. all uncertainty is modeled by probabilities. \item\end{CompactList}\item 
class {\bf epdf}
\begin{CompactList}\small\item\em Probability density function with numerical statistics, e.g. posterior density. \item\end{CompactList}\item 
class {\bf mpdf}
\begin{CompactList}\small\item\em Conditional probability density, e.g. modeling some dependencies. \item\end{CompactList}\item 
class {\bf DS}
\begin{CompactList}\small\item\em Abstract class for discrete-time sources of data. \item\end{CompactList}\end{CompactItemize}


\subsection{Detailed Description}
Bayesian Models (bm) that use Bayes rule to learn from observations. 

\begin{Desc}
\item[Author:]Vaclav Smidl.\end{Desc}
----------------------------------- BDM++ - C++ library for Bayesian Decision Making under Uncertainty

Using IT++ for numerical operations -----------------------------------