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1\section{work/mixpp/bdm/stat/libBM.h File Reference}
2\label{libBM_8h}\index{work/mixpp/bdm/stat/libBM.h@{work/mixpp/bdm/stat/libBM.h}}
3Bayesian Models (bm) that use Bayes rule to learn from observations.
4
5{\tt \#include $<$itpp/itbase.h$>$}\par
6
7
8Include dependency graph for libBM.h:\nopagebreak
9\begin{figure}[H]
10\begin{center}
11\leavevmode
12\includegraphics[width=100pt]{libBM_8h__incl}
13\end{center}
14\end{figure}
15
16
17This graph shows which files directly or indirectly include this file:\nopagebreak
18\begin{figure}[H]
19\begin{center}
20\leavevmode
21\includegraphics[width=361pt]{libBM_8h__dep__incl}
22\end{center}
23\end{figure}
24\subsection*{Classes}
25\begin{CompactItemize}
26\item 
27class {\bf RV}
28\begin{CompactList}\small\item\em Class representing variables, most often random variables. \item\end{CompactList}\item 
29class {\bf fnc}
30\begin{CompactList}\small\item\em Class representing function \$f(x)\$ of variable \$x\$ represented by {\tt rv}. \item\end{CompactList}\item 
31class {\bf BM}
32\begin{CompactList}\small\item\em Bayesian Model of the world, i.e. all uncertainty is modeled by probabilities. \item\end{CompactList}\item 
33class {\bf epdf}
34\begin{CompactList}\small\item\em Probability density function with numerical statistics, e.g. posterior density. \item\end{CompactList}\item 
35class {\bf mpdf}
36\begin{CompactList}\small\item\em Conditional probability density, e.g. modeling some dependencies. \item\end{CompactList}\item 
37class {\bf DS}
38\begin{CompactList}\small\item\em Abstract class for discrete-time sources of data. \item\end{CompactList}\end{CompactItemize}
39
40
41\subsection{Detailed Description}
42Bayesian Models (bm) that use Bayes rule to learn from observations.
43
44\begin{Desc}
45\item[Author:]Vaclav Smidl.\end{Desc}
46----------------------------------- BDM++ - C++ library for Bayesian Decision Making under Uncertainty
47
48Using IT++ for numerical operations -----------------------------------
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