\section{mgamma\_\-fix Class Reference}
\label{classmgamma__fix}\index{mgamma\_\-fix@{mgamma\_\-fix}}
Gamma random walk around a fixed point.  


{\tt \#include $<$libEF.h$>$}

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\subsection*{Public Member Functions}
\begin{CompactItemize}
\item 
{\bf mgamma\_\-fix} (const {\bf RV} \&{\bf rv}, const {\bf RV} \&{\bf rvc})\label{classmgamma__fix_b92c3d2e5fd0381033a072e5ef3bcf80}

\begin{CompactList}\small\item\em Constructor. \item\end{CompactList}\item 
void {\bf set\_\-parameters} (double k0, vec ref0, double l0)\label{classmgamma__fix_ec6f846896749e27cb7be9fa48dd1cb1}

\begin{CompactList}\small\item\em Set value of {\tt k}. \item\end{CompactList}\item 
void {\bf condition} (const vec \&val)\label{classmgamma__fix_6ea3931eec7b7da7b693e45981052460}

\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 
void {\bf set\_\-parameters} (double {\bf k})\label{classmgamma_a9d646cf758a70126dde7c48790b6e94}

\begin{CompactList}\small\item\em Set value of {\tt k}. \item\end{CompactList}\item 
virtual vec {\bf samplecond} (const vec \&cond, double \&ll)
\begin{CompactList}\small\item\em Returns the required moment of the \doxyref{epdf}{p.}{classepdf}. \item\end{CompactList}\item 
virtual mat {\bf samplecond} (const vec \&cond, vec \&ll, int N)
\begin{CompactList}\small\item\em Returns. \item\end{CompactList}\item 
virtual double {\bf evalcond} (const vec \&dt, const vec \&cond)\label{classmpdf_80b738ece5bd4f8c4edaee4b38906f91}

\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 
{\bf RV} {\bf \_\-rvc} ()\label{classmpdf_ec9c850305984582548e8deb64f0ffe8}

\begin{CompactList}\small\item\em access function \item\end{CompactList}\item 
{\bf RV} {\bf \_\-rv} ()\label{classmpdf_1e71ad4c66d5884c82d4a3b06b42fe32}

\begin{CompactList}\small\item\em access function \item\end{CompactList}\item 
{\bf epdf} \& {\bf \_\-epdf} ()\label{classmpdf_e17780ee5b2cfe05922a6c56af1462f8}

\begin{CompactList}\small\item\em access function \item\end{CompactList}\end{CompactItemize}
\subsection*{Protected Attributes}
\begin{CompactItemize}
\item 
double {\bf l}\label{classmgamma__fix_3f48c09caddc298901ad75fe7c0529f6}

\begin{CompactList}\small\item\em parameter l \item\end{CompactList}\item 
vec {\bf refl}\label{classmgamma__fix_81ce49029ecc385418619b200dcafeb0}

\begin{CompactList}\small\item\em reference vector \item\end{CompactList}\item 
{\bf egamma} {\bf epdf}\label{classmgamma_612dbf35c770a780027619aaac2c443e}

\begin{CompactList}\small\item\em Internal \doxyref{epdf}{p.}{classepdf} that arise by conditioning on {\tt rvc}. \item\end{CompactList}\item 
double {\bf k}\label{classmgamma_43f733cce0245a52363d566099add687}

\begin{CompactList}\small\item\em Constant $k$. \item\end{CompactList}\item 
vec $\ast$ {\bf \_\-beta}\label{classmgamma_5e90652837448bcc29707e7412f99691}

\begin{CompactList}\small\item\em cache of epdf.beta \item\end{CompactList}\item 
{\bf RV} {\bf rv}\label{classmpdf_f6687c07ff07d47812dd565368ca59eb}

\begin{CompactList}\small\item\em modeled random variable \item\end{CompactList}\item 
{\bf RV} {\bf rvc}\label{classmpdf_acb7dda792b3cd5576f39fa3129abbab}

\begin{CompactList}\small\item\em random variable in condition \item\end{CompactList}\item 
{\bf epdf} $\ast$ {\bf ep}\label{classmpdf_7aa894208a32f3487827df6d5054424c}

\begin{CompactList}\small\item\em pointer to internal \doxyref{epdf}{p.}{classepdf} \item\end{CompactList}\end{CompactItemize}


\subsection{Detailed Description}
Gamma random walk around a fixed point. 

Mean value, $\mu$, of this density is given by a geometric combination of {\tt rvc} and given fixed point, $p$. $l$ is the coefficient of the geometric combimation \[ \mu = \mu_{t-1} ^{l} p^{1-l}\]

Standard deviation of the random walk is proportional to one $k$-th the mean. This is achieved by setting $\alpha=k$ and $\beta=k/\mu$.

The standard deviation of the walk is then: $\mu/\sqrt(k)$. 

\subsection{Member Function Documentation}
\index{mgamma\_\-fix@{mgamma\_\-fix}!samplecond@{samplecond}}
\index{samplecond@{samplecond}!mgamma_fix@{mgamma\_\-fix}}
\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}


Returns the required moment of the \doxyref{epdf}{p.}{classepdf}. 

Returns a sample from the density conditioned on {\tt cond}, $x \sim epdf(rv|cond)$. \begin{Desc}
\item[Parameters:]
\begin{description}
\item[{\em cond}]is numeric value of {\tt rv} \item[{\em ll}]is a return value of log-likelihood of the sample. \end{description}
\end{Desc}


References mpdf::condition(), mpdf::ep, epdf::evalpdflog(), and epdf::sample().

Referenced by MPF$<$ BM\_\-T $>$::bayes(), and PF::bayes().\index{mgamma\_\-fix@{mgamma\_\-fix}!samplecond@{samplecond}}
\index{samplecond@{samplecond}!mgamma_fix@{mgamma\_\-fix}}
\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}


Returns. 

\begin{Desc}
\item[Parameters:]
\begin{description}
\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}
\end{Desc}


References mpdf::condition(), RV::count(), mpdf::ep, epdf::evalpdflog(), mpdf::rv, and epdf::sample().

The documentation for this class was generated from the following file:\begin{CompactItemize}
\item 
work/git/mixpp/bdm/stat/{\bf libEF.h}\end{CompactItemize}
