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\section{mgamma Class Reference}
\label{classmgamma}\index{mgamma@{mgamma}}
Gamma random walk.  


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

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Collaboration diagram for mgamma:\nopagebreak
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\subsection*{Public Member Functions}
\begin{CompactItemize}
\item 
{\bf mgamma} (const {\bf RV} \&{\bf rv}, const {\bf RV} \&{\bf rvc})\label{classmgamma_af43e61b86900c0398d5c0ffc83b94e6}

\begin{CompactList}\small\item\em Constructor. \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 
vec {\bf samplecond} (vec \&cond, double \&lik)\label{classmgamma_9f40dc43885085fad8e3d6652b79e139}

\begin{CompactList}\small\item\em Generate one sample of the posterior. \item\end{CompactList}\item 
mat {\bf samplecond} (vec \&cond, vec \&lik, int n)\label{classmgamma_e9d52749793f40aad85b70c6db4435ae}

\begin{CompactList}\small\item\em Generate matrix of samples of the posterior. \item\end{CompactList}\item 
void {\bf condition} (const vec \&val)\label{classmgamma_a61094c9f7a2d64ea77b130cbc031f97}

\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 
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 epdf} \& {\bf \_\-epdf} ()\label{classmpdf_e17780ee5b2cfe05922a6c56af1462f8}

\begin{CompactList}\small\item\em access function \item\end{CompactList}\end{CompactItemize}
\subsection*{Protected Attributes}
\begin{CompactItemize}
\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. 

Mean value, $\mu$, of this density is given by {\tt rvc} . 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: $\mu/\sqrt(k)$. 

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