\hypertarget{classeDirich}{ \section{eDirich Class Reference} \label{classeDirich}\index{eDirich@{eDirich}} } Dirichlet posterior density. {\tt \#include $<$libEF.h$>$} Inheritance diagram for eDirich:\nopagebreak \begin{figure}[H] \begin{center} \leavevmode \includegraphics[width=46pt]{classeDirich__inherit__graph} \end{center} \end{figure} Collaboration diagram for eDirich:\nopagebreak \begin{figure}[H] \begin{center} \leavevmode \includegraphics[width=46pt]{classeDirich__coll__graph} \end{center} \end{figure} \subsection*{Public Member Functions} \begin{CompactItemize} \item \hypertarget{classeDirich_ac7e6116f3575c3860d07355e96cd4af}{ \hyperlink{classeDirich_ac7e6116f3575c3860d07355e96cd4af}{eDirich} (const \hyperlink{classRV}{RV} \&\hyperlink{classepdf_74da992e3f5d598da8850b646b79b9d9}{rv}, const vec \&beta0)} \label{classeDirich_ac7e6116f3575c3860d07355e96cd4af} \begin{CompactList}\small\item\em Default constructor. \item\end{CompactList}\item \hypertarget{classeDirich_55cccbc5eb44764dce722567acf5fd58}{ \hyperlink{classeDirich_55cccbc5eb44764dce722567acf5fd58}{eDirich} (const \hyperlink{classeDirich}{eDirich} \&D0)} \label{classeDirich_55cccbc5eb44764dce722567acf5fd58} \begin{CompactList}\small\item\em Copy constructor. \item\end{CompactList}\item \hypertarget{classeDirich_23dff79110822e9639343fe8e177fd80}{ vec \hyperlink{classeDirich_23dff79110822e9639343fe8e177fd80}{sample} () const } \label{classeDirich_23dff79110822e9639343fe8e177fd80} \begin{CompactList}\small\item\em Returns a sample, $x$ from density $epdf(rv)$. \item\end{CompactList}\item \hypertarget{classeDirich_4206e1da149d51ff3b663c9241096b73}{ vec \hyperlink{classeDirich_4206e1da149d51ff3b663c9241096b73}{mean} () const } \label{classeDirich_4206e1da149d51ff3b663c9241096b73} \begin{CompactList}\small\item\em return expected value \item\end{CompactList}\item \hypertarget{classeDirich_688a24f04be6d80d4769cf0e4ded7acc}{ double \hyperlink{classeDirich_688a24f04be6d80d4769cf0e4ded7acc}{evalpdflog\_\-nn} (const vec \&val) const } \label{classeDirich_688a24f04be6d80d4769cf0e4ded7acc} \begin{CompactList}\small\item\em In this instance, val is ... \item\end{CompactList}\item \hypertarget{classeDirich_7ce60be7119ffc639ede4e583c1f6e77}{ double \hyperlink{classeDirich_7ce60be7119ffc639ede4e583c1f6e77}{lognc} () const } \label{classeDirich_7ce60be7119ffc639ede4e583c1f6e77} \begin{CompactList}\small\item\em logarithm of the normalizing constant, $\mathcal{I}$ \item\end{CompactList}\item \hypertarget{classeDirich_6409d0362143a23976b43641ff19e53a}{ vec \& \hyperlink{classeDirich_6409d0362143a23976b43641ff19e53a}{\_\-beta} ()} \label{classeDirich_6409d0362143a23976b43641ff19e53a} \begin{CompactList}\small\item\em access function \item\end{CompactList}\item \hypertarget{classeDirich_c842acb2e1cce5cc9000769ff06c086d}{ void \hyperlink{classeDirich_c842acb2e1cce5cc9000769ff06c086d}{set\_\-parameters} (const vec \&beta0)} \label{classeDirich_c842acb2e1cce5cc9000769ff06c086d} \begin{CompactList}\small\item\em Set internal parameters. \item\end{CompactList}\item \hypertarget{classeEF_a89bef8996410609004fa019b5b48964}{ virtual void \hyperlink{classeEF_a89bef8996410609004fa019b5b48964}{dupdate} (mat \&v)} \label{classeEF_a89bef8996410609004fa019b5b48964} \begin{CompactList}\small\item\em TODO decide if it is really needed. \item\end{CompactList}\item \hypertarget{classeEF_6466e8d4aa9dd64698ed288cbb1afc03}{ virtual double \hyperlink{classeEF_6466e8d4aa9dd64698ed288cbb1afc03}{evalpdflog} (const vec \&val) const } \label{classeEF_6466e8d4aa9dd64698ed288cbb1afc03} \begin{CompactList}\small\item\em Evaluate normalized log-probability. \item\end{CompactList}\item \hypertarget{classeEF_c71faf4b2d153efda14bf1f87dca1507}{ virtual vec \hyperlink{classeEF_c71faf4b2d153efda14bf1f87dca1507}{evalpdflog} (const mat \&Val) const } \label{classeEF_c71faf4b2d153efda14bf1f87dca1507} \begin{CompactList}\small\item\em Evaluate normalized log-probability for many samples. \item\end{CompactList}\item \hypertarget{classeEF_4f8385dd1cc9740522dc373b1dc3cbf5}{ virtual void \hyperlink{classeEF_4f8385dd1cc9740522dc373b1dc3cbf5}{pow} (double p)} \label{classeEF_4f8385dd1cc9740522dc373b1dc3cbf5} \begin{CompactList}\small\item\em Power of the density, used e.g. to flatten the density. \item\end{CompactList}\item \hypertarget{classepdf_54d7dd53a641b618771cd9bee135181f}{ virtual mat \hyperlink{classepdf_54d7dd53a641b618771cd9bee135181f}{sampleN} (int N) const } \label{classepdf_54d7dd53a641b618771cd9bee135181f} \begin{CompactList}\small\item\em Returns N samples from density $epdf(rv)$. \item\end{CompactList}\item \hypertarget{classepdf_3ea597362e11a0040fe7c990269d072c}{ virtual double \hyperlink{classepdf_3ea597362e11a0040fe7c990269d072c}{eval} (const vec \&val) const } \label{classepdf_3ea597362e11a0040fe7c990269d072c} \begin{CompactList}\small\item\em Compute probability of argument {\tt val}. \item\end{CompactList}\item \hypertarget{classepdf_cebbdd7a85e6328f7358fc0ba8eee06c}{ virtual vec \hyperlink{classepdf_cebbdd7a85e6328f7358fc0ba8eee06c}{evalpdflog\_\-m} (const mat \&Val) const } \label{classepdf_cebbdd7a85e6328f7358fc0ba8eee06c} \begin{CompactList}\small\item\em Compute log-probability of multiple values argument {\tt val}. \item\end{CompactList}\item \hypertarget{classepdf_ca0d32aabb4cbba347e0c37fe8607562}{ const \hyperlink{classRV}{RV} \& \hyperlink{classepdf_ca0d32aabb4cbba347e0c37fe8607562}{\_\-rv} () const } \label{classepdf_ca0d32aabb4cbba347e0c37fe8607562} \begin{CompactList}\small\item\em access function, possibly dangerous! \item\end{CompactList}\item \hypertarget{classepdf_7fb94ce90d1ac7077d29f7d6a6c3e0a5}{ void \hyperlink{classepdf_7fb94ce90d1ac7077d29f7d6a6c3e0a5}{\_\-renewrv} (const \hyperlink{classRV}{RV} \&in\_\-rv)} \label{classepdf_7fb94ce90d1ac7077d29f7d6a6c3e0a5} \begin{CompactList}\small\item\em modifier function - useful when copying epdfs \item\end{CompactList}\end{CompactItemize} \subsection*{Protected Attributes} \begin{CompactItemize} \item \hypertarget{classeDirich_15e6b65e9595eedc8a1286c6cecd36d7}{ vec \hyperlink{classeDirich_15e6b65e9595eedc8a1286c6cecd36d7}{beta}} \label{classeDirich_15e6b65e9595eedc8a1286c6cecd36d7} \begin{CompactList}\small\item\em sufficient statistics \item\end{CompactList}\item \hypertarget{classepdf_74da992e3f5d598da8850b646b79b9d9}{ \hyperlink{classRV}{RV} \hyperlink{classepdf_74da992e3f5d598da8850b646b79b9d9}{rv}} \label{classepdf_74da992e3f5d598da8850b646b79b9d9} \begin{CompactList}\small\item\em Identified of the random variable. \item\end{CompactList}\end{CompactItemize} \subsection{Detailed Description} Dirichlet posterior density. Continuous Dirichlet density of $n$-dimensional variable $x$ \[ f(x|\beta) = \frac{\Gamma[\gamma]}{\prod_{i=1}^{n}\Gamma(\beta_i)} \prod_{i=1}^{n}x_i^{\beta_i-1} \] where $\gamma=\sum_i \beta_i$. The documentation for this class was generated from the following file:\begin{CompactItemize} \item work/git/mixpp/bdm/stat/\hyperlink{libEF_8h}{libEF.h}\end{CompactItemize}