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09/04/08 20:27:01 (16 years ago)
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smidl
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opravy a dokumentace

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  • doc/latex/classmgamma__fix.tex

    r145 r162  
    3535 
    3636\begin{CompactList}\small\item\em Set value of {\tt k}. \item\end{CompactList}\item  
    37 vec {\bf samplecond} (vec \&cond, double \&lik)\label{classmgamma_9f40dc43885085fad8e3d6652b79e139} 
    38  
    39 \begin{CompactList}\small\item\em Generate one sample of the posterior. \item\end{CompactList}\item  
    40 mat {\bf samplecond} (vec \&cond, vec \&lik, int n)\label{classmgamma_e9d52749793f40aad85b70c6db4435ae} 
    41  
    42 \begin{CompactList}\small\item\em Generate matrix of samples of the posterior. \item\end{CompactList}\item  
     37virtual vec {\bf samplecond} (const vec \&cond, double \&ll) 
     38\begin{CompactList}\small\item\em Returns the required moment of the \doxyref{epdf}{p.}{classepdf}. \item\end{CompactList}\item  
     39virtual mat {\bf samplecond} (const vec \&cond, vec \&ll, int N) 
     40\begin{CompactList}\small\item\em Returns. \item\end{CompactList}\item  
    4341virtual double {\bf evalcond} (const vec \&dt, const vec \&cond)\label{classmpdf_80b738ece5bd4f8c4edaee4b38906f91} 
    4442 
    4543\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  
    4644{\bf RV} {\bf \_\-rvc} ()\label{classmpdf_ec9c850305984582548e8deb64f0ffe8} 
     45 
     46\begin{CompactList}\small\item\em access function \item\end{CompactList}\item  
     47{\bf RV} {\bf \_\-rv} ()\label{classmpdf_1e71ad4c66d5884c82d4a3b06b42fe32} 
    4748 
    4849\begin{CompactList}\small\item\em access function \item\end{CompactList}\item  
    … …  
    8889The standard deviation of the walk is then: $\mu/\sqrt(k)$.  
    8990 
     91\subsection{Member Function Documentation} 
     92\index{mgamma\_\-fix@{mgamma\_\-fix}!samplecond@{samplecond}} 
     93\index{samplecond@{samplecond}!mgamma_fix@{mgamma\_\-fix}} 
     94\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} 
     95 
     96 
     97Returns the required moment of the \doxyref{epdf}{p.}{classepdf}.  
     98 
     99Returns a sample from the density conditioned on {\tt cond}, $x \sim epdf(rv|cond)$. \begin{Desc} 
     100\item[Parameters:] 
     101\begin{description} 
     102\item[{\em cond}]is numeric value of {\tt rv} \item[{\em ll}]is a return value of log-likelihood of the sample. \end{description} 
     103\end{Desc} 
     104 
     105 
     106References mpdf::condition(), mpdf::ep, epdf::evalpdflog(), and epdf::sample(). 
     107 
     108Referenced by MPF$<$ BM\_\-T $>$::bayes(), and PF::bayes().\index{mgamma\_\-fix@{mgamma\_\-fix}!samplecond@{samplecond}} 
     109\index{samplecond@{samplecond}!mgamma_fix@{mgamma\_\-fix}} 
     110\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} 
     111 
     112 
     113Returns.  
     114 
     115\begin{Desc} 
     116\item[Parameters:] 
     117\begin{description} 
     118\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} 
     119\end{Desc} 
     120 
     121 
     122References mpdf::condition(), RV::count(), mpdf::ep, epdf::evalpdflog(), mpdf::rv, and epdf::sample(). 
     123 
    90124The documentation for this class was generated from the following file:\begin{CompactItemize} 
    91125\item