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1\section{MPF$<$ BM\_\-T $>$ Class Template Reference}
2\label{classMPF}\index{MPF@{MPF}}
3Marginalized Particle filter. 
4
5
6{\tt \#include $<$libPF.h$>$}
7
8Inheritance diagram for MPF$<$ BM\_\-T $>$:\nopagebreak
9\begin{figure}[H]
10\begin{center}
11\leavevmode
12\includegraphics[width=62pt]{classMPF__inherit__graph}
13\end{center}
14\end{figure}
15Collaboration diagram for MPF$<$ BM\_\-T $>$:\nopagebreak
16\begin{figure}[H]
17\begin{center}
18\leavevmode
19\includegraphics[width=159pt]{classMPF__coll__graph}
20\end{center}
21\end{figure}
22\subsection*{Public Member Functions}
23\begin{CompactItemize}
24\item 
25{\bf MPF} (const {\bf RV} \&rvlin, const {\bf RV} \&rvpf, {\bf mpdf} \&par0, {\bf mpdf} \&obs0, int {\bf n}, const BM\_\-T \&BMcond0)\label{classMPF_fc5e11e11eec3195e3c6503937bf02bd}
26
27\begin{CompactList}\small\item\em Default constructor. \item\end{CompactList}\item 
28void {\bf bayes} (const vec \&dt)
29\begin{CompactList}\small\item\em Incremental Bayes rule. \item\end{CompactList}\item 
30{\bf epdf} \& {\bf \_\-epdf} ()\label{classMPF_549e08268a46a250f21a33d06f19276a}
31
32\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 
33void {\bf set\_\-est} (const {\bf epdf} \&epdf0)\label{classMPF_7c66e1c1c0e45fc4ae765133cb3a1553}
34
35\begin{CompactList}\small\item\em Set postrior of {\tt rvc} to samples from epdf0. Statistics of Bms are not re-computed! Use only for initialization! \item\end{CompactList}\item 
36void {\bf bayes} (mat Dt)\label{classBM_87b07867fd4c133aa89a18543f68d9f9}
37
38\begin{CompactList}\small\item\em Batch Bayes rule (columns of Dt are observations). \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*{Public Attributes}
46\begin{CompactItemize}
47\item 
48double \textbf{SSAT}\label{classMPF_65b869f3cde2e8d3cfcc2752d14d1ca6}
49
50\end{CompactItemize}
51\subsection*{Protected Attributes}
52\begin{CompactItemize}
53\item 
54int {\bf n}\label{classPF_2c2f44ed7a4eaa42e07bdb58d503f280}
55
56\begin{CompactList}\small\item\em number of particles; \item\end{CompactList}\item 
57{\bf eEmp} {\bf est}\label{classPF_1a0a09e309da997f63ae8e30d1e9806b}
58
59\begin{CompactList}\small\item\em posterior density \item\end{CompactList}\item 
60vec \& {\bf \_\-w}\label{classPF_5c87aba508df321ff26536ced64dbb3a}
61
62\begin{CompactList}\small\item\em pointer into {\tt \doxyref{eEmp}{p.}{classeEmp}} \item\end{CompactList}\item 
63Array$<$ vec $>$ \& {\bf \_\-samples}\label{classPF_cf7dad75e31215780a746c30e71ad9c5}
64
65\begin{CompactList}\small\item\em pointer into {\tt \doxyref{eEmp}{p.}{classeEmp}} \item\end{CompactList}\item 
66{\bf mpdf} \& {\bf par}\label{classPF_d92ac103f88f8c21e197e90af5695a09}
67
68\begin{CompactList}\small\item\em Parameter evolution model. \item\end{CompactList}\item 
69{\bf mpdf} \& {\bf obs}\label{classPF_dd0a687a4515333d6809147335854e77}
70
71\begin{CompactList}\small\item\em Observation model. \item\end{CompactList}\item 
72{\bf RV} {\bf rv}\label{classBM_af00f0612fabe66241dd507188cdbf88}
73
74\begin{CompactList}\small\item\em Random variable of the posterior. \item\end{CompactList}\item 
75double {\bf ll}\label{classBM_5623fef6572a08c2b53b8c87b82dc979}
76
77\begin{CompactList}\small\item\em Logarithm of marginalized data likelihood. \item\end{CompactList}\item 
78bool {\bf evalll}\label{classBM_bf6fb59b30141074f8ee1e2f43d03129}
79
80\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}
81\subsection*{Classes}
82\begin{CompactItemize}
83\item 
84class \textbf{mpfepdf}
85\begin{CompactList}\small\item\em internal class for MPDF providing composition of \doxyref{eEmp}{p.}{classeEmp} with external components \item\end{CompactList}\end{CompactItemize}
86
87
88\subsection{Detailed Description}
89\subsubsection*{template$<$class BM\_\-T$>$ class MPF$<$ BM\_\-T $>$}
90
91Marginalized Particle filter.
92
93Trivial version: proposal = parameter evolution, observation model is not used. (it is assumed to be part of \doxyref{BM}{p.}{classBM}).
94
95\subsection{Member Function Documentation}
96\index{MPF@{MPF}!bayes@{bayes}}
97\index{bayes@{bayes}!MPF@{MPF}}
98\subsubsection[bayes]{\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}
99
100
101Incremental Bayes rule.
102
103\begin{Desc}
104\item[Parameters:]
105\begin{description}
106\item[{\em dt}]vector of input data \end{description}
107\end{Desc}
108
109
110Reimplemented from {\bf PF} \doxyref{}{p.}{classPF_64f636bbd63bea9efd778214e6b631d3}.
111
112References PF::\_\-samples, PF::\_\-w, PF::est, PF::n, PF::par, eEmp::resample(), and mpdf::samplecond().
113
114The documentation for this class was generated from the following file:\begin{CompactItemize}
115\item 
116work/git/mixpp/bdm/estim/{\bf libPF.h}\end{CompactItemize}
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