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1\section{mpdf Class Reference}
2\label{classmpdf}\index{mpdf@{mpdf}}
3Conditional probability density, e.g. modeling some dependencies. 
4
5
6{\tt \#include $<$libBM.h$>$}
7
8Inheritance diagram for mpdf:\nopagebreak
9\begin{figure}[H]
10\begin{center}
11\leavevmode
12\includegraphics[width=137pt]{classmpdf__inherit__graph}
13\end{center}
14\end{figure}
15Collaboration diagram for mpdf:\nopagebreak
16\begin{figure}[H]
17\begin{center}
18\leavevmode
19\includegraphics[width=56pt]{classmpdf__coll__graph}
20\end{center}
21\end{figure}
22\subsection*{Public Member Functions}
23\begin{CompactItemize}
24\item 
25virtual vec {\bf samplecond} (vec \&cond, double \&ll)
26\begin{CompactList}\small\item\em Returns the required moment of the \doxyref{epdf}{p.}{classepdf}. \item\end{CompactList}\item 
27virtual mat {\bf samplecond} (vec \&cond, vec \&ll, int N)
28\begin{CompactList}\small\item\em Returns. \item\end{CompactList}\item 
29virtual void {\bf condition} (const vec \&cond)\label{classmpdf_0f95a0cc6ab40611f46804682446ed83}
30
31\begin{CompactList}\small\item\em Update {\tt ep} so that it represents this \doxyref{mpdf}{p.}{classmpdf} conditioned on {\tt rvc} = cond. \item\end{CompactList}\item 
32virtual double {\bf evalcond} (const vec \&dt, const vec \&cond)\label{classmpdf_80b738ece5bd4f8c4edaee4b38906f91}
33
34\begin{CompactList}\small\item\em Shortcut for conditioning and evaluation of the internal \doxyref{epdf}{p.}{classepdf}. In some cases, this operation can be implemented efficiently. \item\end{CompactList}\item 
35virtual {\bf $\sim$mpdf} ()\label{classmpdf_6788be9f3a888796499c5293a318fcfb}
36
37\begin{CompactList}\small\item\em Destructor for future use;. \item\end{CompactList}\item 
38{\bf mpdf} (const {\bf RV} \&rv0, const {\bf RV} \&rvc0)\label{classmpdf_581ecf362185d37c08bb31cb9d046d6f}
39
40\begin{CompactList}\small\item\em Default constructor. \item\end{CompactList}\item 
41{\bf RV} {\bf \_\-rvc} ()\label{classmpdf_ec9c850305984582548e8deb64f0ffe8}
42
43\begin{CompactList}\small\item\em access function \item\end{CompactList}\item 
44{\bf epdf} \& {\bf \_\-epdf} ()\label{classmpdf_e17780ee5b2cfe05922a6c56af1462f8}
45
46\begin{CompactList}\small\item\em access function \item\end{CompactList}\end{CompactItemize}
47\subsection*{Protected Attributes}
48\begin{CompactItemize}
49\item 
50{\bf RV} {\bf rv}\label{classmpdf_f6687c07ff07d47812dd565368ca59eb}
51
52\begin{CompactList}\small\item\em modeled random variable \item\end{CompactList}\item 
53{\bf RV} {\bf rvc}\label{classmpdf_acb7dda792b3cd5576f39fa3129abbab}
54
55\begin{CompactList}\small\item\em random variable in condition \item\end{CompactList}\item 
56{\bf epdf} $\ast$ {\bf ep}\label{classmpdf_7aa894208a32f3487827df6d5054424c}
57
58\begin{CompactList}\small\item\em pointer to internal \doxyref{epdf}{p.}{classepdf} \item\end{CompactList}\end{CompactItemize}
59
60
61\subsection{Detailed Description}
62Conditional probability density, e.g. modeling some dependencies.
63
64\subsection{Member Function Documentation}
65\index{mpdf@{mpdf}!samplecond@{samplecond}}
66\index{samplecond@{samplecond}!mpdf@{mpdf}}
67\subsubsection[samplecond]{\setlength{\rightskip}{0pt plus 5cm}virtual vec mpdf::samplecond (vec \& {\em cond}, \/  double \& {\em ll})\hspace{0.3cm}{\tt  [inline, virtual]}}\label{classmpdf_b0193a350c97933ddf15b15a130da352}
68
69
70Returns the required moment of the \doxyref{epdf}{p.}{classepdf}.
71
72Returns a sample from the density conditioned on {\tt cond}, $x \sim epdf(rv|cond)$. \begin{Desc}
73\item[Parameters:]
74\begin{description}
75\item[{\em cond}]is numeric value of {\tt rv} \item[{\em ll}]is a return value of log-likelihood of the sample. \end{description}
76\end{Desc}
77
78
79Reimplemented in {\bf mlnorm$<$ sq\_\-T $>$} \doxyref{}{p.}{classmlnorm_decf3e3b5c8e0812e5b4dbe94fa2ae18}, and {\bf mgamma} \doxyref{}{p.}{classmgamma_9f40dc43885085fad8e3d6652b79e139}.
80
81References condition(), ep, epdf::evalpdflog(), and epdf::sample().
82
83Referenced by MPF$<$ BM\_\-T $>$::bayes(), and PF::bayes().\index{mpdf@{mpdf}!samplecond@{samplecond}}
84\index{samplecond@{samplecond}!mpdf@{mpdf}}
85\subsubsection[samplecond]{\setlength{\rightskip}{0pt plus 5cm}virtual mat mpdf::samplecond (vec \& {\em cond}, \/  vec \& {\em ll}, \/  int {\em N})\hspace{0.3cm}{\tt  [inline, virtual]}}\label{classmpdf_6bf806badfdac606c847e458e8fce18c}
86
87
88Returns.
89
90\begin{Desc}
91\item[Parameters:]
92\begin{description}
93\item[{\em N}]samples from the density conditioned on {\tt cond}, $x \sim epdf(rv|cond)$. \item[{\em cond}]is numeric value of {\tt rv} \item[{\em ll}]is a return value of log-likelihood of the sample. \end{description}
94\end{Desc}
95
96
97Reimplemented in {\bf mlnorm$<$ sq\_\-T $>$} \doxyref{}{p.}{classmlnorm_215fb88cc8b95d64cdefd6849abdd1e8}, and {\bf mgamma} \doxyref{}{p.}{classmgamma_e9d52749793f40aad85b70c6db4435ae}.
98
99References condition(), RV::count(), ep, epdf::evalpdflog(), rv, and epdf::sample().
100
101The documentation for this class was generated from the following file:\begin{CompactItemize}
102\item 
103work/git/mixpp/bdm/stat/{\bf libBM.h}\end{CompactItemize}
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