\section{BM Class Reference}
\label{classBM}\index{BM@{BM}}
Bayesian Model of the world, i.e. all uncertainty is modeled by probabilities.  


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

Inheritance diagram for BM:\nopagebreak
\begin{figure}[H]
\begin{center}
\leavevmode
\includegraphics[width=192pt]{classBM__inherit__graph}
\end{center}
\end{figure}
Collaboration diagram for BM:\nopagebreak
\begin{figure}[H]
\begin{center}
\leavevmode
\includegraphics[width=38pt]{classBM__coll__graph}
\end{center}
\end{figure}
\subsection*{Public Member Functions}
\begin{CompactItemize}
\item 
{\bf BM} (const {\bf RV} \&rv0)\label{classBM_605d28b426adb677c86a57ddb525132a}

\begin{CompactList}\small\item\em Default constructor. \item\end{CompactList}\item 
virtual void {\bf bayes} (const vec \&dt)=0
\begin{CompactList}\small\item\em Incremental Bayes rule. \item\end{CompactList}\item 
void {\bf bayes} (mat Dt)\label{classBM_87b07867fd4c133aa89a18543f68d9f9}

\begin{CompactList}\small\item\em Batch Bayes rule (columns of Dt are observations). \item\end{CompactList}\item 
virtual {\bf epdf} \& {\bf \_\-epdf} ()=0\label{classBM_3dc45554556926bde996a267636abe55}

\begin{CompactList}\small\item\em Returns a pointer to the \doxyref{epdf}{p.}{classepdf} representing posterior density on parameters. Use with care! \item\end{CompactList}\item 
virtual {\bf $\sim$BM} ()\label{classBM_ca0f02b3b4144e0895cc14f7e0374bdd}

\begin{CompactList}\small\item\em Destructor for future use;. \item\end{CompactList}\item 
const {\bf RV} \& {\bf \_\-rv} () const \label{classBM_126bd2595c48e311fc2a7ab72876092a}

\begin{CompactList}\small\item\em access function \item\end{CompactList}\item 
double {\bf \_\-ll} () const \label{classBM_87f4a547d2c29180be88175e5eab9c88}

\begin{CompactList}\small\item\em access function \item\end{CompactList}\end{CompactItemize}
\subsection*{Protected Attributes}
\begin{CompactItemize}
\item 
{\bf RV} {\bf rv}\label{classBM_af00f0612fabe66241dd507188cdbf88}

\begin{CompactList}\small\item\em Random variable of the posterior. \item\end{CompactList}\item 
double {\bf ll}\label{classBM_5623fef6572a08c2b53b8c87b82dc979}

\begin{CompactList}\small\item\em Logarithm of marginalized data likelihood. \item\end{CompactList}\item 
bool {\bf evalll}\label{classBM_bf6fb59b30141074f8ee1e2f43d03129}

\begin{CompactList}\small\item\em If true, the filter will compute likelihood of the data record and store it in {\tt ll} . Set to false if you want to save time. \item\end{CompactList}\end{CompactItemize}


\subsection{Detailed Description}
Bayesian Model of the world, i.e. all uncertainty is modeled by probabilities. 

\subsection{Member Function Documentation}
\index{BM@{BM}!bayes@{bayes}}
\index{bayes@{bayes}!BM@{BM}}
\subsubsection{\setlength{\rightskip}{0pt plus 5cm}virtual void BM::bayes (const vec \& {\em dt})\hspace{0.3cm}{\tt  [pure virtual]}}\label{classBM_a892eff438aab2dd1a9e2efcb7fb5bdf}


Incremental Bayes rule. 

\begin{Desc}
\item[Parameters:]
\begin{description}
\item[{\em dt}]vector of input data \end{description}
\end{Desc}


Implemented in {\bf Kalman$<$ sq\_\-T $>$} \doxyref{}{p.}{classKalman_7750ffd73f261828a32c18aaeb65c75c}, {\bf KalmanCh} \doxyref{}{p.}{classKalmanCh_cca758192846940409822b9bd778d4e1}, {\bf EKF$<$ sq\_\-T $>$} \doxyref{}{p.}{classEKF_c79c62c9b3e0b56b3aaa1b6f1d9a7af7}, {\bf PF} \doxyref{}{p.}{classPF_64f636bbd63bea9efd778214e6b631d3}, {\bf MPF$<$ BM\_\-T $>$} \doxyref{}{p.}{classMPF_55daf8e4b6553dd9f47c692de7931623}, {\bf Kalman$<$ ldmat $>$} \doxyref{}{p.}{classKalman_7750ffd73f261828a32c18aaeb65c75c}, and {\bf EKF$<$ ldmat $>$} \doxyref{}{p.}{classEKF_c79c62c9b3e0b56b3aaa1b6f1d9a7af7}.

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