root/doc/latex/classMPF.tex @ 31

\section{MPF$<$ BM\_\-T $>$ Class Template Reference}
\label{classMPF}\index{MPF@{MPF}}
Marginalized Particle filter.  


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

Inheritance diagram for MPF$<$ BM\_\-T $>$:\nopagebreak
\begin{figure}[H]
\begin{center}
\leavevmode
\includegraphics[width=65pt]{classMPF__inherit__graph}
\end{center}
\end{figure}
Collaboration diagram for MPF$<$ BM\_\-T $>$:\nopagebreak
\begin{figure}[H]
\begin{center}
\leavevmode
\includegraphics[width=130pt]{classMPF__coll__graph}
\end{center}
\end{figure}
\subsection*{Public Member Functions}
\begin{CompactItemize}
\item 
{\bf MPF} (const {\bf RV} \&rv0, {\bf mpdf} \&par0, {\bf mpdf} \&obs0, int {\bf n}, const BM\_\-T \&BMcond0)\label{classMPF_827a66609cf69a832535d52233f76fa0}

\begin{CompactList}\small\item\em Default constructor. \item\end{CompactList}\item 
void {\bf bayes} (const vec \&dt)
\begin{CompactList}\small\item\em Incremental Bayes rule. \item\end{CompactList}\item 
{\bf epdf} \& {\bf \_\-epdf} ()\label{classMPF_549e08268a46a250f21a33d06f19276a}

\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 
void \textbf{set\_\-est} (const {\bf epdf} $\ast$\&epdf0)\label{classPF_c5caa2c15604338b773d7a8125e7a1b5}

\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}\end{CompactItemize}
\subsection*{Protected Attributes}
\begin{CompactItemize}
\item 
int {\bf n}\label{classPF_2c2f44ed7a4eaa42e07bdb58d503f280}

\begin{CompactList}\small\item\em number of particles; \item\end{CompactList}\item 
{\bf eEmp} {\bf ePdf}\label{classPF_a2ac56d1e3ffbb4ff0b3f02e6399deb0}

\begin{CompactList}\small\item\em posterior density \item\end{CompactList}\item 
vec \& {\bf w}\label{classPF_a97d12da4d1832c0b0c6ec5877f921f0}

\begin{CompactList}\small\item\em pointer into {\tt \doxyref{eEmp}{p.}{classeEmp}} \item\end{CompactList}\item 
Array$<$ vec $>$ \& {\bf samples}\label{classPF_361743a0b5b89de1a29e91d1343b2565}

\begin{CompactList}\small\item\em pointer into {\tt \doxyref{eEmp}{p.}{classeEmp}} \item\end{CompactList}\item 
{\bf mpdf} \& {\bf par}\label{classPF_d92ac103f88f8c21e197e90af5695a09}

\begin{CompactList}\small\item\em Parameter evolution model. \item\end{CompactList}\item 
{\bf mpdf} \& {\bf obs}\label{classPF_dd0a687a4515333d6809147335854e77}

\begin{CompactList}\small\item\em Observation model. \item\end{CompactList}\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}
\subsubsection*{template$<$class BM\_\-T$>$ class MPF$<$ BM\_\-T $>$}

Marginalized Particle filter. 

\subsection{Member Function Documentation}
\index{MPF@{MPF}!bayes@{bayes}}
\index{bayes@{bayes}!MPF@{MPF}}
\subsubsection{\setlength{\rightskip}{0pt plus 5cm}template$<$class BM\_\-T$>$ void {\bf MPF}$<$ BM\_\-T $>$::bayes (const vec \& {\em dt})\hspace{0.3cm}{\tt  [inline, virtual]}}\label{classMPF_55daf8e4b6553dd9f47c692de7931623}


Incremental Bayes rule. 

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


Reimplemented from {\bf PF} \doxyref{}{p.}{classPF_64f636bbd63bea9efd778214e6b631d3}.

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