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1\hypertarget{classPF}{
2\section{PF Class Reference}
3\label{classPF}\index{PF@{PF}}
4}
5Trivial particle filter with proposal density equal to parameter evolution model. 
6
7
8{\tt \#include $<$libPF.h$>$}
9
10Inheritance diagram for PF:\nopagebreak
11\begin{figure}[H]
12\begin{center}
13\leavevmode
14\includegraphics[width=62pt]{classPF__inherit__graph}
15\end{center}
16\end{figure}
17Collaboration diagram for PF:\nopagebreak
18\begin{figure}[H]
19\begin{center}
20\leavevmode
21\includegraphics[width=92pt]{classPF__coll__graph}
22\end{center}
23\end{figure}
24\subsection*{Public Member Functions}
25\begin{CompactItemize}
26\item 
27\hypertarget{classPF_e99f0d866721405dd281e315ecb690aa}{
28\hyperlink{classPF_e99f0d866721405dd281e315ecb690aa}{PF} (const \hyperlink{classRV}{RV} \&rv0, \hyperlink{classmpdf}{mpdf} \&par0, \hyperlink{classmpdf}{mpdf} \&obs0, int n0)}
29\label{classPF_e99f0d866721405dd281e315ecb690aa}
30
31\begin{CompactList}\small\item\em Default constructor. \item\end{CompactList}\item 
32\hypertarget{classPF_04d38fbcc0348b558212f530d9ec183e}{
33void \hyperlink{classPF_04d38fbcc0348b558212f530d9ec183e}{set\_\-est} (const \hyperlink{classepdf}{epdf} \&epdf0)}
34\label{classPF_04d38fbcc0348b558212f530d9ec183e}
35
36\begin{CompactList}\small\item\em Set posterior density by sampling from epdf0. \item\end{CompactList}\item 
37void \hyperlink{classPF_64f636bbd63bea9efd778214e6b631d3}{bayes} (const vec \&dt)
38\begin{CompactList}\small\item\em Incremental Bayes rule. \item\end{CompactList}\item 
39\hypertarget{classBM_0186270f75189677f390fe088a9947e9}{
40virtual void \hyperlink{classBM_0186270f75189677f390fe088a9947e9}{bayesB} (const mat \&Dt)}
41\label{classBM_0186270f75189677f390fe088a9947e9}
42
43\begin{CompactList}\small\item\em Batch Bayes rule (columns of Dt are observations). \item\end{CompactList}\item 
44\hypertarget{classBM_8554809fec160bee279bcd5fa14ead5c}{
45virtual const \hyperlink{classepdf}{epdf} \& \hyperlink{classBM_8554809fec160bee279bcd5fa14ead5c}{\_\-epdf} () const =0}
46\label{classBM_8554809fec160bee279bcd5fa14ead5c}
47
48\begin{CompactList}\small\item\em Returns a reference to the \hyperlink{classepdf}{epdf} representing posterior density on parameters. \item\end{CompactList}\item 
49\hypertarget{classBM_db500c4836f883c175a3cf3ab5a3ce24}{
50virtual const \hyperlink{classepdf}{epdf} $\ast$ \hyperlink{classBM_db500c4836f883c175a3cf3ab5a3ce24}{\_\-e} () const =0}
51\label{classBM_db500c4836f883c175a3cf3ab5a3ce24}
52
53\begin{CompactList}\small\item\em Returns a pointer to the \hyperlink{classepdf}{epdf} representing posterior density on parameters. Use with care! \item\end{CompactList}\item 
54virtual double \hyperlink{classBM_8a8ce6df431689964c41cc6c849cfd06}{logpred} (const vec \&dt) const
55\item 
56\hypertarget{classBM_cd0660f2a1a344b56ac39802708ff165}{
57vec \hyperlink{classBM_cd0660f2a1a344b56ac39802708ff165}{logpred\_\-m} (const mat \&dt) const }
58\label{classBM_cd0660f2a1a344b56ac39802708ff165}
59
60\begin{CompactList}\small\item\em Matrix version of logpred. \item\end{CompactList}\item 
61\hypertarget{classBM_5594d68ee9aa6fc8c1e79019da5c9d56}{
62virtual \hyperlink{classepdf}{epdf} $\ast$ \hyperlink{classBM_5594d68ee9aa6fc8c1e79019da5c9d56}{predictor} (const \hyperlink{classRV}{RV} \&\hyperlink{classBM_af00f0612fabe66241dd507188cdbf88}{rv}) const }
63\label{classBM_5594d68ee9aa6fc8c1e79019da5c9d56}
64
65\begin{CompactList}\small\item\em Constructs a predictive density (marginal density on data). \item\end{CompactList}\item 
66\hypertarget{classBM_126bd2595c48e311fc2a7ab72876092a}{
67const \hyperlink{classRV}{RV} \& \hyperlink{classBM_126bd2595c48e311fc2a7ab72876092a}{\_\-rv} () const }
68\label{classBM_126bd2595c48e311fc2a7ab72876092a}
69
70\begin{CompactList}\small\item\em access function \item\end{CompactList}\item 
71\hypertarget{classBM_87f4a547d2c29180be88175e5eab9c88}{
72double \hyperlink{classBM_87f4a547d2c29180be88175e5eab9c88}{\_\-ll} () const }
73\label{classBM_87f4a547d2c29180be88175e5eab9c88}
74
75\begin{CompactList}\small\item\em access function \item\end{CompactList}\item 
76\hypertarget{classBM_1ffa9f23669aabecc3760c06c6987522}{
77void \hyperlink{classBM_1ffa9f23669aabecc3760c06c6987522}{set\_\-evalll} (bool evl0)}
78\label{classBM_1ffa9f23669aabecc3760c06c6987522}
79
80\begin{CompactList}\small\item\em access function \item\end{CompactList}\item 
81virtual \hyperlink{classBM}{BM} $\ast$ \hyperlink{classBM_eb58c81d6a7b75b05fc6f276eed78887}{\_\-copy\_\-} (bool changerv=false)
82\end{CompactItemize}
83\subsection*{Protected Attributes}
84\begin{CompactItemize}
85\item 
86\hypertarget{classPF_2c2f44ed7a4eaa42e07bdb58d503f280}{
87int \hyperlink{classPF_2c2f44ed7a4eaa42e07bdb58d503f280}{n}}
88\label{classPF_2c2f44ed7a4eaa42e07bdb58d503f280}
89
90\begin{CompactList}\small\item\em number of particles; \item\end{CompactList}\item 
91\hypertarget{classPF_1a0a09e309da997f63ae8e30d1e9806b}{
92\hyperlink{classeEmp}{eEmp} \hyperlink{classPF_1a0a09e309da997f63ae8e30d1e9806b}{est}}
93\label{classPF_1a0a09e309da997f63ae8e30d1e9806b}
94
95\begin{CompactList}\small\item\em posterior density \item\end{CompactList}\item 
96\hypertarget{classPF_5c87aba508df321ff26536ced64dbb3a}{
97vec \& \hyperlink{classPF_5c87aba508df321ff26536ced64dbb3a}{\_\-w}}
98\label{classPF_5c87aba508df321ff26536ced64dbb3a}
99
100\begin{CompactList}\small\item\em pointer into {\tt \hyperlink{classeEmp}{eEmp}} \item\end{CompactList}\item 
101\hypertarget{classPF_cf7dad75e31215780a746c30e71ad9c5}{
102Array$<$ vec $>$ \& \hyperlink{classPF_cf7dad75e31215780a746c30e71ad9c5}{\_\-samples}}
103\label{classPF_cf7dad75e31215780a746c30e71ad9c5}
104
105\begin{CompactList}\small\item\em pointer into {\tt \hyperlink{classeEmp}{eEmp}} \item\end{CompactList}\item 
106\hypertarget{classPF_d92ac103f88f8c21e197e90af5695a09}{
107\hyperlink{classmpdf}{mpdf} \& \hyperlink{classPF_d92ac103f88f8c21e197e90af5695a09}{par}}
108\label{classPF_d92ac103f88f8c21e197e90af5695a09}
109
110\begin{CompactList}\small\item\em Parameter evolution model. \item\end{CompactList}\item 
111\hypertarget{classPF_dd0a687a4515333d6809147335854e77}{
112\hyperlink{classmpdf}{mpdf} \& \hyperlink{classPF_dd0a687a4515333d6809147335854e77}{obs}}
113\label{classPF_dd0a687a4515333d6809147335854e77}
114
115\begin{CompactList}\small\item\em Observation model. \item\end{CompactList}\item 
116\hypertarget{classBM_af00f0612fabe66241dd507188cdbf88}{
117\hyperlink{classRV}{RV} \hyperlink{classBM_af00f0612fabe66241dd507188cdbf88}{rv}}
118\label{classBM_af00f0612fabe66241dd507188cdbf88}
119
120\begin{CompactList}\small\item\em Random variable of the posterior. \item\end{CompactList}\item 
121\hypertarget{classBM_5623fef6572a08c2b53b8c87b82dc979}{
122double \hyperlink{classBM_5623fef6572a08c2b53b8c87b82dc979}{ll}}
123\label{classBM_5623fef6572a08c2b53b8c87b82dc979}
124
125\begin{CompactList}\small\item\em Logarithm of marginalized data likelihood. \item\end{CompactList}\item 
126\hypertarget{classBM_bf6fb59b30141074f8ee1e2f43d03129}{
127bool \hyperlink{classBM_bf6fb59b30141074f8ee1e2f43d03129}{evalll}}
128\label{classBM_bf6fb59b30141074f8ee1e2f43d03129}
129
130\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 computational time. \item\end{CompactList}\end{CompactItemize}
131
132
133\subsection{Detailed Description}
134Trivial particle filter with proposal density equal to parameter evolution model.
135
136Posterior density is represented by a weighted empirical density ({\tt \hyperlink{classeEmp}{eEmp}} ).
137
138\subsection{Member Function Documentation}
139\hypertarget{classPF_64f636bbd63bea9efd778214e6b631d3}{
140\index{PF@{PF}!bayes@{bayes}}
141\index{bayes@{bayes}!PF@{PF}}
142\subsubsection[bayes]{\setlength{\rightskip}{0pt plus 5cm}void PF::bayes (const vec \& {\em dt})\hspace{0.3cm}{\tt  \mbox{[}virtual\mbox{]}}}}
143\label{classPF_64f636bbd63bea9efd778214e6b631d3}
144
145
146Incremental Bayes rule.
147
148\begin{Desc}
149\item[Parameters:]
150\begin{description}
151\item[{\em dt}]vector of input data \end{description}
152\end{Desc}
153
154
155Implements \hyperlink{classBM_a892eff438aab2dd1a9e2efcb7fb5bdf}{BM}.
156
157Reimplemented in \hyperlink{classMPF_55daf8e4b6553dd9f47c692de7931623}{MPF$<$ BM\_\-T $>$}.
158
159References \_\-samples, \_\-w, est, mpdf::evalcond(), n, obs, par, eEmp::resample(), and mpdf::samplecond().\hypertarget{classBM_8a8ce6df431689964c41cc6c849cfd06}{
160\index{PF@{PF}!logpred@{logpred}}
161\index{logpred@{logpred}!PF@{PF}}
162\subsubsection[logpred]{\setlength{\rightskip}{0pt plus 5cm}virtual double BM::logpred (const vec \& {\em dt}) const\hspace{0.3cm}{\tt  \mbox{[}inline, virtual, inherited\mbox{]}}}}
163\label{classBM_8a8ce6df431689964c41cc6c849cfd06}
164
165
166Evaluates predictive log-likelihood of the given data record I.e. marginal likelihood of the data with the posterior integrated out.
167
168Reimplemented in \hyperlink{classARX_e7f9e7823aec9bf7ddc3b42d9b3304c4}{ARX}, \hyperlink{classMixEF_424ca64f36d4e41de7a7e7ae921d35ea}{MixEF}, and \hyperlink{classmultiBM_13e26a61757278981fd8cac9a7ef91eb}{multiBM}.
169
170Referenced by BM::logpred\_\-m().\hypertarget{classBM_eb58c81d6a7b75b05fc6f276eed78887}{
171\index{PF@{PF}!\_\-copy\_\-@{\_\-copy\_\-}}
172\index{\_\-copy\_\-@{\_\-copy\_\-}!PF@{PF}}
173\subsubsection[\_\-copy\_\-]{\setlength{\rightskip}{0pt plus 5cm}virtual {\bf BM}$\ast$ BM::\_\-copy\_\- (bool {\em changerv} = {\tt false})\hspace{0.3cm}{\tt  \mbox{[}inline, virtual, inherited\mbox{]}}}}
174\label{classBM_eb58c81d6a7b75b05fc6f276eed78887}
175
176
177Copy function required in vectors, Arrays of \hyperlink{classBM}{BM} etc. Have to be DELETED manually! Prototype: BM$\ast$ \hyperlink{classBM_eb58c81d6a7b75b05fc6f276eed78887}{\_\-copy\_\-()}\{\hyperlink{classBM}{BM} Tmp$\ast$=new Tmp(this$\ast$); return Tmp; \} 
178
179Reimplemented in \hyperlink{classARX_5de61fbd4f97fa3216760b1f733f5af0}{ARX}, and \hyperlink{classBMEF_97f5312efe4a5bedb86d2daec59d8651}{BMEF}.
180
181The documentation for this class was generated from the following files:\begin{CompactItemize}
182\item 
183work/git/mixpp/bdm/estim/\hyperlink{libPF_8h}{libPF.h}\item 
184work/git/mixpp/bdm/estim/libPF.cpp\end{CompactItemize}
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