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

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


Include dependency graph for libBM.h:\nopagebreak
\begin{figure}[H]
\begin{center}
\leavevmode
\includegraphics[width=106pt]{libBM_8h__incl}
\end{center}
\end{figure}


This graph shows which files directly or indirectly include this file:\nopagebreak
\begin{figure}[H]
\begin{center}
\leavevmode
\includegraphics[width=420pt]{libBM_8h__dep__incl}
\end{center}
\end{figure}
\subsection*{Classes}
\begin{CompactItemize}
\item 
class \hyperlink{classstr}{str}
\begin{CompactList}\small\item\em Structure of \hyperlink{classRV}{RV} (used internally), i.e. expanded RVs. \item\end{CompactList}\item 
class \hyperlink{classRV}{RV}
\begin{CompactList}\small\item\em Class representing variables, most often random variables. \item\end{CompactList}\item 
class \hyperlink{classfnc}{fnc}
\begin{CompactList}\small\item\em Class representing function $f(x)$ of variable $x$ represented by {\tt rv}. \item\end{CompactList}\item 
class \hyperlink{classepdf}{epdf}
\begin{CompactList}\small\item\em Probability density function with numerical statistics, e.g. posterior density. \item\end{CompactList}\item 
class \hyperlink{classmpdf}{mpdf}
\begin{CompactList}\small\item\em Conditional probability density, e.g. modeling some dependencies. \item\end{CompactList}\item 
class \hyperlink{classmepdf}{mepdf}
\begin{CompactList}\small\item\em Unconditional \hyperlink{classmpdf}{mpdf}, allows using \hyperlink{classepdf}{epdf} in the role of \hyperlink{classmpdf}{mpdf}. \item\end{CompactList}\item 
class \hyperlink{classcompositepdf}{compositepdf}
\begin{CompactList}\small\item\em Abstract composition of pdfs, a base for specific classes. \item\end{CompactList}\item 
class \hyperlink{classDS}{DS}
\begin{CompactList}\small\item\em Abstract class for discrete-time sources of data. \item\end{CompactList}\item 
class \hyperlink{classBM}{BM}
\begin{CompactList}\small\item\em Bayesian Model of the world, i.e. all uncertainty is modeled by probabilities. \item\end{CompactList}\item 
class \hyperlink{classBMcond}{BMcond}
\begin{CompactList}\small\item\em Conditional Bayesian Filter. \item\end{CompactList}\end{CompactItemize}
\subsection*{Functions}
\begin{CompactItemize}
\item 
\hypertarget{libBM_8h_33c114e83980d883c5b211c47d5322a4}{
\hyperlink{classRV}{RV} \hyperlink{libBM_8h_33c114e83980d883c5b211c47d5322a4}{concat} (const \hyperlink{classRV}{RV} \&rv1, const \hyperlink{classRV}{RV} \&rv2)}
\label{libBM_8h_33c114e83980d883c5b211c47d5322a4}

\begin{CompactList}\small\item\em Concat two random variables. \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 -----------------------------------