\section{eEmp Class Reference}
\label{classeEmp}\index{eEmp@{eEmp}}
Weighted empirical density.  


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

Inheritance diagram for eEmp:\nopagebreak
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Collaboration diagram for eEmp:\nopagebreak
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\subsection*{Public Member Functions}
\begin{CompactItemize}
\item 
{\bf eEmp} (const {\bf RV} \&rv0, int n0)\label{classeEmp_0c04b073ecd0dae3d498e680ae27e9e4}

\begin{CompactList}\small\item\em Default constructor. \item\end{CompactList}\item 
void {\bf set\_\-parameters} (const vec \&w0, {\bf epdf} $\ast$pdf0)\label{classeEmp_6606a656c1b28114f7384c25aaf80e8d}

\begin{CompactList}\small\item\em Set sample. \item\end{CompactList}\item 
vec \& {\bf \_\-w} ()\label{classeEmp_31b2bfb73b72486a5c89f2ab850c7a9b}

\begin{CompactList}\small\item\em Potentially dangerous, use with care. \item\end{CompactList}\item 
Array$<$ vec $>$ \& {\bf \_\-samples} ()\label{classeEmp_31b747eca73b16f30370827ba4cc3575}

\begin{CompactList}\small\item\em access function \item\end{CompactList}\item 
ivec {\bf resample} ({\bf RESAMPLING\_\-METHOD} method=SYSTEMATIC)\label{classeEmp_77268292fc4465cb73ddbfb1f2932a59}

\begin{CompactList}\small\item\em Function performs resampling, i.e. removal of low-weight samples and duplication of high-weight samples such that the new samples represent the same density. \item\end{CompactList}\item 
vec {\bf sample} () const \label{classeEmp_83f9283f92b805508d896479dc1ccf12}

\begin{CompactList}\small\item\em inherited operation : NOT implemneted \item\end{CompactList}\item 
double {\bf evalpdflog} (const vec \&val) const \label{classeEmp_23e7358995400865ad2e278945922fb3}

\begin{CompactList}\small\item\em inherited operation : NOT implemneted \item\end{CompactList}\item 
vec {\bf mean} () const \label{classeEmp_ba055c19038cc72628d98e25197e982d}

\begin{CompactList}\small\item\em return expected value \item\end{CompactList}\item 
virtual mat {\bf sampleN} (int N) const \label{classepdf_54d7dd53a641b618771cd9bee135181f}

\begin{CompactList}\small\item\em Returns N samples from density $epdf(rv)$. \item\end{CompactList}\item 
virtual double {\bf eval} (const vec \&val) const \label{classepdf_3ea597362e11a0040fe7c990269d072c}

\begin{CompactList}\small\item\em Compute probability of argument {\tt val}. \item\end{CompactList}\item 
{\bf RV} {\bf \_\-rv} () const \label{classepdf_b89143f12c9b49282e30841e4fb5f337}

\begin{CompactList}\small\item\em access function \item\end{CompactList}\end{CompactItemize}
\subsection*{Protected Attributes}
\begin{CompactItemize}
\item 
int {\bf n}\label{classeEmp_8c33034de0e35f03f8bb85d3d67438fd}

\begin{CompactList}\small\item\em Number of particles. \item\end{CompactList}\item 
vec {\bf w}\label{classeEmp_ae78d144404ddba843c93b171b215de8}

\begin{CompactList}\small\item\em Sample weights $w$. \item\end{CompactList}\item 
Array$<$ vec $>$ {\bf samples}\label{classeEmp_a4d6f4bbd6a6824fc39f14676701279a}

\begin{CompactList}\small\item\em Samples $x^{(i)}, i=1..n$. \item\end{CompactList}\item 
{\bf RV} {\bf rv}\label{classepdf_74da992e3f5d598da8850b646b79b9d9}

\begin{CompactList}\small\item\em Identified of the random variable. \item\end{CompactList}\end{CompactItemize}


\subsection{Detailed Description}
Weighted empirical density. 

Used e.g. in particle filters. 

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