\hypertarget{emix_8h}{
\section{work/git/mixpp/bdm/stat/emix.h File Reference}
\label{emix_8h}\index{work/git/mixpp/bdm/stat/emix.h@{work/git/mixpp/bdm/stat/emix.h}}
}
Probability distributions for Mixtures of pdfs. 

{\tt \#include \char`\"{}libBM.h\char`\"{}}\par
{\tt \#include \char`\"{}libEF.h\char`\"{}}\par


Include dependency graph for emix.h:\nopagebreak
\begin{figure}[H]
\begin{center}
\leavevmode
\includegraphics[width=138pt]{emix_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=218pt]{emix_8h__dep__incl}
\end{center}
\end{figure}
\subsection*{Namespaces}
\begin{CompactItemize}
\item 
namespace \textbf{bdm}
\end{CompactItemize}
\subsection*{Classes}
\begin{CompactItemize}
\item 
class \hyperlink{classbdm_1_1mratio}{bdm::mratio}
\begin{CompactList}\small\item\em Class representing ratio of two densities which arise e.g. by applying the Bayes rule. It represents density in the form: \[ f(rv|rvc) = \frac{f(rv,rvc)}{f(rvc)} \] where $ f(rvc) = \int f(rv,rvc) d\ rv $. \item\end{CompactList}\item 
class \hyperlink{classbdm_1_1emix}{bdm::emix}
\begin{CompactList}\small\item\em Mixture of epdfs. \item\end{CompactList}\item 
class \hyperlink{classbdm_1_1mprod}{bdm::mprod}
\begin{CompactList}\small\item\em Chain rule decomposition of \hyperlink{classbdm_1_1epdf}{epdf}. \item\end{CompactList}\item 
class \hyperlink{classbdm_1_1eprod}{bdm::eprod}
\begin{CompactList}\small\item\em Product of independent epdfs. For dependent pdfs, use \hyperlink{classbdm_1_1mprod}{mprod}. \item\end{CompactList}\item 
class \hyperlink{classbdm_1_1mmix}{bdm::mmix}
\begin{CompactList}\small\item\em Mixture of mpdfs with constant weights, all mpdfs are of equal type. \item\end{CompactList}\end{CompactItemize}


\subsection{Detailed Description}
Probability distributions for Mixtures of pdfs. 

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

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