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1\section{PF Class Reference}
2\label{classPF}\index{PF@{PF}}
3Trivial particle filter with proposal density equal to parameter evolution model. 
4
5
6{\tt \#include $<$libPF.h$>$}
7
8Inheritance diagram for PF:\nopagebreak
9\begin{figure}[H]
10\begin{center}
11\leavevmode
12\includegraphics[width=62pt]{classPF__inherit__graph}
13\end{center}
14\end{figure}
15Collaboration diagram for PF:\nopagebreak
16\begin{figure}[H]
17\begin{center}
18\leavevmode
19\includegraphics[width=92pt]{classPF__coll__graph}
20\end{center}
21\end{figure}
22\subsection*{Public Member Functions}
23\begin{CompactItemize}
24\item 
25{\bf PF} (const {\bf RV} \&rv0, {\bf mpdf} \&par0, {\bf mpdf} \&obs0, int n0)\label{classPF_e99f0d866721405dd281e315ecb690aa}
26
27\begin{CompactList}\small\item\em Default constructor. \item\end{CompactList}\item 
28void {\bf set\_\-est} (const {\bf epdf} \&epdf0)\label{classPF_04d38fbcc0348b558212f530d9ec183e}
29
30\begin{CompactList}\small\item\em Set posterior density by sampling from epdf0. \item\end{CompactList}\item 
31void {\bf bayes} (const vec \&dt)
32\begin{CompactList}\small\item\em Incremental Bayes rule. \item\end{CompactList}\item 
33void {\bf bayes} (mat Dt)\label{classBM_87b07867fd4c133aa89a18543f68d9f9}
34
35\begin{CompactList}\small\item\em Batch Bayes rule (columns of Dt are observations). \item\end{CompactList}\item 
36virtual {\bf epdf} \& {\bf \_\-epdf} ()=0\label{classBM_3dc45554556926bde996a267636abe55}
37
38\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 
39const {\bf RV} \& {\bf \_\-rv} () const \label{classBM_126bd2595c48e311fc2a7ab72876092a}
40
41\begin{CompactList}\small\item\em access function \item\end{CompactList}\item 
42double {\bf \_\-ll} () const \label{classBM_87f4a547d2c29180be88175e5eab9c88}
43
44\begin{CompactList}\small\item\em access function \item\end{CompactList}\end{CompactItemize}
45\subsection*{Protected Attributes}
46\begin{CompactItemize}
47\item 
48int {\bf n}\label{classPF_2c2f44ed7a4eaa42e07bdb58d503f280}
49
50\begin{CompactList}\small\item\em number of particles; \item\end{CompactList}\item 
51{\bf eEmp} {\bf est}\label{classPF_1a0a09e309da997f63ae8e30d1e9806b}
52
53\begin{CompactList}\small\item\em posterior density \item\end{CompactList}\item 
54vec \& {\bf \_\-w}\label{classPF_5c87aba508df321ff26536ced64dbb3a}
55
56\begin{CompactList}\small\item\em pointer into {\tt \doxyref{eEmp}{p.}{classeEmp}} \item\end{CompactList}\item 
57Array$<$ vec $>$ \& {\bf \_\-samples}\label{classPF_cf7dad75e31215780a746c30e71ad9c5}
58
59\begin{CompactList}\small\item\em pointer into {\tt \doxyref{eEmp}{p.}{classeEmp}} \item\end{CompactList}\item 
60{\bf mpdf} \& {\bf par}\label{classPF_d92ac103f88f8c21e197e90af5695a09}
61
62\begin{CompactList}\small\item\em Parameter evolution model. \item\end{CompactList}\item 
63{\bf mpdf} \& {\bf obs}\label{classPF_dd0a687a4515333d6809147335854e77}
64
65\begin{CompactList}\small\item\em Observation model. \item\end{CompactList}\item 
66{\bf RV} {\bf rv}\label{classBM_af00f0612fabe66241dd507188cdbf88}
67
68\begin{CompactList}\small\item\em Random variable of the posterior. \item\end{CompactList}\item 
69double {\bf ll}\label{classBM_5623fef6572a08c2b53b8c87b82dc979}
70
71\begin{CompactList}\small\item\em Logarithm of marginalized data likelihood. \item\end{CompactList}\item 
72bool {\bf evalll}\label{classBM_bf6fb59b30141074f8ee1e2f43d03129}
73
74\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}
75
76
77\subsection{Detailed Description}
78Trivial particle filter with proposal density equal to parameter evolution model.
79
80Posterior density is represented by a weighted empirical density ({\tt \doxyref{eEmp}{p.}{classeEmp}} ).
81
82\subsection{Member Function Documentation}
83\index{PF@{PF}!bayes@{bayes}}
84\index{bayes@{bayes}!PF@{PF}}
85\subsubsection[bayes]{\setlength{\rightskip}{0pt plus 5cm}void PF::bayes (const vec \& {\em dt})\hspace{0.3cm}{\tt  [virtual]}}\label{classPF_64f636bbd63bea9efd778214e6b631d3}
86
87
88Incremental Bayes rule.
89
90\begin{Desc}
91\item[Parameters:]
92\begin{description}
93\item[{\em dt}]vector of input data \end{description}
94\end{Desc}
95
96
97Implements {\bf BM} \doxyref{}{p.}{classBM_a892eff438aab2dd1a9e2efcb7fb5bdf}.
98
99Reimplemented in {\bf MPF$<$ BM\_\-T $>$} \doxyref{}{p.}{classMPF_55daf8e4b6553dd9f47c692de7931623}.
100
101References \_\-samples, \_\-w, est, mpdf::evalcond(), n, obs, par, eEmp::resample(), and mpdf::samplecond().
102
103The documentation for this class was generated from the following files:\begin{CompactItemize}
104\item 
105work/git/mixpp/bdm/estim/{\bf libPF.h}\item 
106work/git/mixpp/bdm/estim/libPF.cpp\end{CompactItemize}
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