\section{TrivialPF Class Reference}
\label{classTrivialPF}\index{TrivialPF@{TrivialPF}}
Trivial particle filter with proposal density that is not conditioned on the data.  


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

Inheritance diagram for TrivialPF:\nopagebreak
\begin{figure}[H]
\begin{center}
\leavevmode
\includegraphics[width=49pt]{classTrivialPF__inherit__graph}
\end{center}
\end{figure}
Collaboration diagram for TrivialPF:\nopagebreak
\begin{figure}[H]
\begin{center}
\leavevmode
\includegraphics[width=85pt]{classTrivialPF__coll__graph}
\end{center}
\end{figure}
\subsection*{Public Member Functions}
\begin{CompactItemize}
\item 
\textbf{TrivialPF} ({\bf mpdf} \&par, {\bf mpdf} \&obs, {\bf BM} \&prop, int n0)\label{classTrivialPF_c5a420747532e24b25cb0d835288795b}

\item 
\textbf{TrivialPF} ({\bf mpdf} \&par, {\bf mpdf} \&obs, int n0)\label{classTrivialPF_59fc4c55a2d5fbb6bc9a17a9dd9a2e13}

\item 
void \textbf{bayes} (const vec \&dt, bool {\bf evalll})\label{classTrivialPF_77a92bf054d763f806d27fc37a058389}

\item 
ivec {\bf resample} (RESAMPLING\_\-METHOD method=SYSTEMATIC)\label{classPF_a0e26b2f6a5884aca49122f3e4f0cf19}

\begin{CompactList}\small\item\em Returns indexes of particles that should be resampled. The ordering MUST guarantee inplace replacement. (Important for MPF.). \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 
{\bf epdf} $\ast$ {\bf \_\-epdf} ()\label{classPF_53b7cc5a0709b0d40fb68408437c0aa2}

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

\item 
vec \textbf{w}\label{classPF_f6bc92f7979af4513b06b161497ba868}

\item 
Uniform\_\-RNG \textbf{URNG}\label{classPF_3568ca7c3b3175d98b548f496b4c34dd}

\end{CompactItemize}


\subsection{Detailed Description}
Trivial particle filter with proposal density that is not conditioned on the data. 

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

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}