Changeset 162 for doc/latex/classmgamma.tex
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- 09/04/08 20:27:01 (16 years ago)
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doc/latex/classmgamma.tex
r145 r162 29 29 30 30 \begin{CompactList}\small\item\em Set value of {\tt k}. \item\end{CompactList}\item 31 vec {\bf samplecond} (vec \&cond, double \&lik)\label{classmgamma_9f40dc43885085fad8e3d6652b79e139}32 33 \begin{CompactList}\small\item\em Generate one sample of the posterior. \item\end{CompactList}\item34 mat {\bf samplecond} (vec \&cond, vec \&lik, int n)\label{classmgamma_e9d52749793f40aad85b70c6db4435ae}35 36 \begin{CompactList}\small\item\em Generate matrix of samples of the posterior. \item\end{CompactList}\item37 31 void {\bf condition} (const vec \&val)\label{classmgamma_a61094c9f7a2d64ea77b130cbc031f97} 38 32 39 33 \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 34 virtual vec {\bf samplecond} (const vec \&cond, double \&ll) 35 \begin{CompactList}\small\item\em Returns the required moment of the \doxyref{epdf}{p.}{classepdf}. \item\end{CompactList}\item 36 virtual mat {\bf samplecond} (const vec \&cond, vec \&ll, int N) 37 \begin{CompactList}\small\item\em Returns. \item\end{CompactList}\item 40 38 virtual double {\bf evalcond} (const vec \&dt, const vec \&cond)\label{classmpdf_80b738ece5bd4f8c4edaee4b38906f91} 41 39 42 40 \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 43 41 {\bf RV} {\bf \_\-rvc} ()\label{classmpdf_ec9c850305984582548e8deb64f0ffe8} 42 43 \begin{CompactList}\small\item\em access function \item\end{CompactList}\item 44 {\bf RV} {\bf \_\-rv} ()\label{classmpdf_1e71ad4c66d5884c82d4a3b06b42fe32} 44 45 45 46 \begin{CompactList}\small\item\em access function \item\end{CompactList}\item … … 77 78 The standard deviation of the walk is then: $\mu/\sqrt(k)$. 78 79 80 \subsection{Member Function Documentation} 81 \index{mgamma@{mgamma}!samplecond@{samplecond}} 82 \index{samplecond@{samplecond}!mgamma@{mgamma}} 83 \subsubsection[samplecond]{\setlength{\rightskip}{0pt plus 5cm}virtual vec mpdf::samplecond (const vec \& {\em cond}, \/ double \& {\em ll})\hspace{0.3cm}{\tt [inline, virtual, inherited]}}\label{classmpdf_3f172b79ec4a5ebc87898a5381141f1b} 84 85 86 Returns the required moment of the \doxyref{epdf}{p.}{classepdf}. 87 88 Returns a sample from the density conditioned on {\tt cond}, $x \sim epdf(rv|cond)$. \begin{Desc} 89 \item[Parameters:] 90 \begin{description} 91 \item[{\em cond}]is numeric value of {\tt rv} \item[{\em ll}]is a return value of log-likelihood of the sample. \end{description} 92 \end{Desc} 93 94 95 References mpdf::condition(), mpdf::ep, epdf::evalpdflog(), and epdf::sample(). 96 97 Referenced by MPF$<$ BM\_\-T $>$::bayes(), and PF::bayes().\index{mgamma@{mgamma}!samplecond@{samplecond}} 98 \index{samplecond@{samplecond}!mgamma@{mgamma}} 99 \subsubsection[samplecond]{\setlength{\rightskip}{0pt plus 5cm}virtual mat mpdf::samplecond (const vec \& {\em cond}, \/ vec \& {\em ll}, \/ int {\em N})\hspace{0.3cm}{\tt [inline, virtual, inherited]}}\label{classmpdf_0e37163660f93df2a4d723cedb1da89c} 100 101 102 Returns. 103 104 \begin{Desc} 105 \item[Parameters:] 106 \begin{description} 107 \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} 108 \end{Desc} 109 110 111 References mpdf::condition(), RV::count(), mpdf::ep, epdf::evalpdflog(), mpdf::rv, and epdf::sample(). 112 79 113 The documentation for this class was generated from the following files:\begin{CompactItemize} 80 114 \item