Index: doc/latex/classegiw.tex
===================================================================
--- doc/latex/classegiw.tex (revision 162)
+++ doc/latex/classegiw.tex (revision 172)
@@ -1,4 +1,6 @@
+\hypertarget{classegiw}{
 \section{egiw Class Reference}
 \label{classegiw}\index{egiw@{egiw}}
+}
 Gauss-inverse-Wishart density stored in LD form.  
 
@@ -23,49 +25,115 @@
 \begin{CompactItemize}
 \item 
-{\bf egiw} ({\bf RV} {\bf rv}, mat V0, double nu0)\label{classegiw_c52a2173c6eb1490edce9c6c7c05d60b}
+\hypertarget{classegiw_c52a2173c6eb1490edce9c6c7c05d60b}{
+\hyperlink{classegiw_c52a2173c6eb1490edce9c6c7c05d60b}{egiw} (\hyperlink{classRV}{RV} \hyperlink{classepdf_74da992e3f5d598da8850b646b79b9d9}{rv}, mat V0, double nu0)}
+\label{classegiw_c52a2173c6eb1490edce9c6c7c05d60b}
 
-\begin{CompactList}\small\item\em Default constructor. \item\end{CompactList}\item 
-vec {\bf sample} () const 
-\begin{CompactList}\small\item\em Returns the required moment of the \doxyref{epdf}{p.}{classepdf}. \item\end{CompactList}\item 
-vec {\bf mean} () const \label{classegiw_6deb0ff2859f41ef7cbdf6a842cabb29}
+\begin{CompactList}\small\item\em Default constructor, assuming. \item\end{CompactList}\item 
+\hypertarget{classegiw_1a17fdbac6c72b9c3abb97623db466c8}{
+\hyperlink{classegiw_1a17fdbac6c72b9c3abb97623db466c8}{egiw} (\hyperlink{classRV}{RV} \hyperlink{classepdf_74da992e3f5d598da8850b646b79b9d9}{rv}, \hyperlink{classldmat}{ldmat} V0, double nu0)}
+\label{classegiw_1a17fdbac6c72b9c3abb97623db466c8}
+
+\begin{CompactList}\small\item\em Full constructor for V in \hyperlink{classldmat}{ldmat} form. \item\end{CompactList}\item 
+\hypertarget{classegiw_3d2c1f2ba0f9966781f1e0ae695e8a6f}{
+vec \hyperlink{classegiw_3d2c1f2ba0f9966781f1e0ae695e8a6f}{sample} () const }
+\label{classegiw_3d2c1f2ba0f9966781f1e0ae695e8a6f}
+
+\begin{CompactList}\small\item\em Returns a sample, $x$ from density $epdf(rv)$. \item\end{CompactList}\item 
+\hypertarget{classegiw_6deb0ff2859f41ef7cbdf6a842cabb29}{
+vec \hyperlink{classegiw_6deb0ff2859f41ef7cbdf6a842cabb29}{mean} () const }
+\label{classegiw_6deb0ff2859f41ef7cbdf6a842cabb29}
 
 \begin{CompactList}\small\item\em return expected value \item\end{CompactList}\item 
-double {\bf evalpdflog} (const vec \&val) const \label{classegiw_425cbc53b377274e28c6add942bab62d}
+\hypertarget{classegiw_9594f396acc5ad186d1c5b03b0745502}{
+void \textbf{mean\_\-mat} (mat \&M, mat \&R) const }
+\label{classegiw_9594f396acc5ad186d1c5b03b0745502}
 
-\begin{CompactList}\small\item\em Compute log-probability of argument {\tt val}. \item\end{CompactList}\item 
-double {\bf lognc} () const \label{classegiw_70eb1a0b88459b227f919b425b0d3359}
+\item 
+\hypertarget{classegiw_2ab1e525d692be8272a6f383d60b94cd}{
+double \hyperlink{classegiw_2ab1e525d692be8272a6f383d60b94cd}{evalpdflog\_\-nn} (const vec \&val) const }
+\label{classegiw_2ab1e525d692be8272a6f383d60b94cd}
+
+\begin{CompactList}\small\item\em In this instance, val= \mbox{[}theta, r\mbox{]}. For multivariate instances, it is stored columnwise val = \mbox{[}theta\_\-1 theta\_\-2 ... r\_\-1 r\_\-2 \mbox{]}. \item\end{CompactList}\item 
+\hypertarget{classegiw_70eb1a0b88459b227f919b425b0d3359}{
+double \hyperlink{classegiw_70eb1a0b88459b227f919b425b0d3359}{lognc} () const }
+\label{classegiw_70eb1a0b88459b227f919b425b0d3359}
 
 \begin{CompactList}\small\item\em logarithm of the normalizing constant, $\mathcal{I}$ \item\end{CompactList}\item 
-{\bf ldmat} \& {\bf \_\-V} ()\label{classegiw_533e792e1175bfa06d5d595dc5d080d5}
+\hypertarget{classegiw_533e792e1175bfa06d5d595dc5d080d5}{
+\hyperlink{classldmat}{ldmat} \& \hyperlink{classegiw_533e792e1175bfa06d5d595dc5d080d5}{\_\-V} ()}
+\label{classegiw_533e792e1175bfa06d5d595dc5d080d5}
 
 \begin{CompactList}\small\item\em returns a pointer to the internal statistics. Use with Care! \item\end{CompactList}\item 
-double \& {\bf \_\-nu} ()\label{classegiw_08029c481ff95d24f093df0573879afe}
+\hypertarget{classegiw_08029c481ff95d24f093df0573879afe}{
+double \& \hyperlink{classegiw_08029c481ff95d24f093df0573879afe}{\_\-nu} ()}
+\label{classegiw_08029c481ff95d24f093df0573879afe}
 
 \begin{CompactList}\small\item\em returns a pointer to the internal statistics. Use with Care! \item\end{CompactList}\item 
-virtual void {\bf tupdate} (double phi, mat \&vbar, double nubar)\label{classeEF_fd88bc35550ec8fe9281d358216d0fcf}
+\hypertarget{classegiw_036306322a90a9977834baac07460816}{
+void \hyperlink{classegiw_036306322a90a9977834baac07460816}{pow} (double p)}
+\label{classegiw_036306322a90a9977834baac07460816}
+
+\begin{CompactList}\small\item\em Power of the density, used e.g. to flatten the density. \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 
-virtual void {\bf dupdate} (mat \&v, double {\bf nu}=1.0)\label{classeEF_5863718c3b2fb1496dece10c5b745d5c}
+\hypertarget{classeEF_6466e8d4aa9dd64698ed288cbb1afc03}{
+virtual double \hyperlink{classeEF_6466e8d4aa9dd64698ed288cbb1afc03}{evalpdflog} (const vec \&val) const }
+\label{classeEF_6466e8d4aa9dd64698ed288cbb1afc03}
 
-\begin{CompactList}\small\item\em TODO decide if it is really needed. \item\end{CompactList}\item 
-virtual mat {\bf sampleN} (int N) const \label{classepdf_54d7dd53a641b618771cd9bee135181f}
+\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{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 
-virtual double {\bf eval} (const vec \&val) const \label{classepdf_3ea597362e11a0040fe7c990269d072c}
+\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 
-{\bf RV} \& {\bf \_\-rv} ()\label{classepdf_4778ea61ef400813e47750e024e9fc2f}
+\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}\end{CompactItemize}
+\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 
-{\bf ldmat} {\bf V}\label{classegiw_f343d03ede89db820edf44a6297fa442}
+\hypertarget{classegiw_f343d03ede89db820edf44a6297fa442}{
+\hyperlink{classldmat}{ldmat} \hyperlink{classegiw_f343d03ede89db820edf44a6297fa442}{V}}
+\label{classegiw_f343d03ede89db820edf44a6297fa442}
 
 \begin{CompactList}\small\item\em Extended information matrix of sufficient statistics. \item\end{CompactList}\item 
-double {\bf nu}\label{classegiw_4a2f130b91afe84f6d62fed289d5d453}
+\hypertarget{classegiw_4a2f130b91afe84f6d62fed289d5d453}{
+double \hyperlink{classegiw_4a2f130b91afe84f6d62fed289d5d453}{nu}}
+\label{classegiw_4a2f130b91afe84f6d62fed289d5d453}
 
 \begin{CompactList}\small\item\em Number of data records (degrees of freedom) of sufficient statistics. \item\end{CompactList}\item 
-{\bf RV} {\bf rv}\label{classepdf_74da992e3f5d598da8850b646b79b9d9}
+\hypertarget{classegiw_3d5c719f15a5527a6c62c2a53160148e}{
+int \hyperlink{classegiw_3d5c719f15a5527a6c62c2a53160148e}{xdim}}
+\label{classegiw_3d5c719f15a5527a6c62c2a53160148e}
+
+\begin{CompactList}\small\item\em Dimension of the output. \item\end{CompactList}\item 
+\hypertarget{classegiw_c70d13d86e0d9f0acede3e1dc0368812}{
+int \hyperlink{classegiw_c70d13d86e0d9f0acede3e1dc0368812}{nPsi}}
+\label{classegiw_c70d13d86e0d9f0acede3e1dc0368812}
+
+\begin{CompactList}\small\item\em Dimension of the regressor. \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}
@@ -75,20 +143,8 @@
 Gauss-inverse-Wishart density stored in LD form. 
 
-More?... 
-
-\subsection{Member Function Documentation}
-\index{egiw@{egiw}!sample@{sample}}
-\index{sample@{sample}!egiw@{egiw}}
-\subsubsection[sample]{\setlength{\rightskip}{0pt plus 5cm}vec egiw::sample () const\hspace{0.3cm}{\tt  [virtual]}}\label{classegiw_3d2c1f2ba0f9966781f1e0ae695e8a6f}
-
-
-Returns the required moment of the \doxyref{epdf}{p.}{classepdf}. 
-
-Returns a sample, $x$ from density $epdf(rv)$ 
-
-Implements {\bf epdf} \doxyref{}{p.}{classepdf_8019654e494bf5e458f6fb947e11b262}.
+For $p$-variate densities, given rv.count() should be $p\times$ V.rows(). 
 
 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/\hyperlink{libEF_8h}{libEF.h}\item 
 work/git/mixpp/bdm/stat/libEF.cpp\end{CompactItemize}
