\section{PF Class Reference}
\label{classPF}\index{PF@{PF}}
Trivial particle filter with proposal density equal to parameter evolution model.  


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

Inheritance diagram for PF:\nopagebreak
\begin{figure}[H]
\begin{center}
\leavevmode
\includegraphics[width=65pt]{classPF__inherit__graph}
\end{center}
\end{figure}
Collaboration diagram for PF:\nopagebreak
\begin{figure}[H]
\begin{center}
\leavevmode
\includegraphics[width=96pt]{classPF__coll__graph}
\end{center}
\end{figure}
\subsection*{Public Member Functions}
\begin{CompactItemize}
\item 
{\bf PF} (const {\bf RV} \&rv0, {\bf mpdf} \&par0, {\bf mpdf} \&obs0, int n0)\label{classPF_e99f0d866721405dd281e315ecb690aa}

\begin{CompactList}\small\item\em Default constructor. \item\end{CompactList}\item 
void {\bf set\_\-est} (const {\bf epdf} \&epdf0)\label{classPF_04d38fbcc0348b558212f530d9ec183e}

\begin{CompactList}\small\item\em Set posterior density by sampling from epdf0. \item\end{CompactList}\item 
void {\bf bayes} (const vec \&dt)
\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 
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 
int {\bf n}\label{classPF_2c2f44ed7a4eaa42e07bdb58d503f280}

\begin{CompactList}\small\item\em number of particles; \item\end{CompactList}\item 
{\bf eEmp} {\bf est}\label{classPF_1a0a09e309da997f63ae8e30d1e9806b}

\begin{CompactList}\small\item\em posterior density \item\end{CompactList}\item 
vec \& {\bf \_\-w}\label{classPF_5c87aba508df321ff26536ced64dbb3a}

\begin{CompactList}\small\item\em pointer into {\tt \doxyref{eEmp}{p.}{classeEmp}} \item\end{CompactList}\item 
Array$<$ vec $>$ \& {\bf \_\-samples}\label{classPF_cf7dad75e31215780a746c30e71ad9c5}

\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}
Trivial particle filter with proposal density equal to parameter evolution model. 

Posterior density is represented by a weighted empirical density ({\tt \doxyref{eEmp}{p.}{classeEmp}} ). 

\subsection{Member Function Documentation}
\index{PF@{PF}!bayes@{bayes}}
\index{bayes@{bayes}!PF@{PF}}
\subsubsection{\setlength{\rightskip}{0pt plus 5cm}void PF::bayes (const vec \& {\em dt})\hspace{0.3cm}{\tt  [virtual]}}\label{classPF_64f636bbd63bea9efd778214e6b631d3}


Incremental Bayes rule. 

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


Implements {\bf BM} \doxyref{}{p.}{classBM_a892eff438aab2dd1a9e2efcb7fb5bdf}.

Reimplemented in {\bf MPF$<$ BM\_\-T $>$} \doxyref{}{p.}{classMPF_55daf8e4b6553dd9f47c692de7931623}.

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