Index: doc/latex/classbdm_1_1ARX.tex
===================================================================
--- doc/latex/classbdm_1_1ARX.tex (revision 269)
+++ doc/latex/classbdm_1_1ARX.tex (revision 270)
@@ -3,7 +3,4 @@
 \label{classbdm_1_1ARX}\index{bdm::ARX@{bdm::ARX}}
 }
-Linear Autoregressive model with Gaussian noise.  
-
-
 {\tt \#include $<$arx.h$>$}
 
@@ -15,17 +12,17 @@
 \end{center}
 \end{figure}
-Collaboration diagram for bdm::ARX:\nopagebreak
-\begin{figure}[H]
-\begin{center}
-\leavevmode
-\includegraphics[height=400pt]{classbdm_1_1ARX__coll__graph}
-\end{center}
-\end{figure}
-\subsection*{Public Member Functions}
-\begin{CompactItemize}
-\item 
-\hypertarget{classbdm_1_1ARX_44914d0b259204b3446db82b989bd626}{
-\hyperlink{classbdm_1_1ARX_44914d0b259204b3446db82b989bd626}{ARX} (const \hyperlink{classbdm_1_1RV}{RV} \&\hyperlink{classbdm_1_1BM_18d6db4af8ee42077741d9e3618153ca}{rv}, const mat \&V0, const double \&nu0, const double frg0=1.0)}
-\label{classbdm_1_1ARX_44914d0b259204b3446db82b989bd626}
+
+
+\subsection{Detailed Description}
+Linear Autoregressive model with Gaussian noise. 
+
+Regression of the following kind: \[ y_t = \theta_1 \psi_1 + \theta_2 + \psi_2 +\ldots + \theta_n \psi_n + r e_t \] where unknown parameters {\tt rv} are $[\theta r]$, regression vector $\psi=\psi(y_{1:t},u_{1:t})$ is a known function of past outputs and exogeneous variables $u_t$. Distrubances $e_t$ are supposed to be normally distributed: \[ e_t \sim \mathcal{N}(0,1). \]
+
+Extension for time-variant parameters $\theta_t,r_t$ may be achived using exponential forgetting (Kulhavy and Zarrop, 1993). In such a case, the forgetting factor {\tt frg} $\in <0,1>$ should be given in the constructor. Time-invariant parameters are estimated for {\tt frg} = 1. \subsection*{Public Member Functions}
+\begin{CompactItemize}
+\item 
+\hypertarget{classbdm_1_1ARX_43ed6114f04a3a8756fe2b42eaa35f98}{
+\hyperlink{classbdm_1_1ARX_43ed6114f04a3a8756fe2b42eaa35f98}{ARX} (const double frg0=1.0)}
+\label{classbdm_1_1ARX_43ed6114f04a3a8756fe2b42eaa35f98}
 
 \begin{CompactList}\small\item\em Full constructor. \item\end{CompactList}\item 
@@ -35,7 +32,7 @@
 
 \begin{CompactList}\small\item\em Copy constructor. \item\end{CompactList}\item 
-\hypertarget{classbdm_1_1ARX_20ff2de8d862f28de7da83444d65bcdb}{
-\hyperlink{classbdm_1_1ARX}{ARX} $\ast$ \hyperlink{classbdm_1_1ARX_20ff2de8d862f28de7da83444d65bcdb}{\_\-copy\_\-} (bool changerv=false)}
-\label{classbdm_1_1ARX_20ff2de8d862f28de7da83444d65bcdb}
+\hypertarget{classbdm_1_1ARX_60c40b5c6abc4c7e464b4ccae64a5a61}{
+\hyperlink{classbdm_1_1ARX}{ARX} $\ast$ \hyperlink{classbdm_1_1ARX_60c40b5c6abc4c7e464b4ccae64a5a61}{\_\-copy\_\-} ()}
+\label{classbdm_1_1ARX_60c40b5c6abc4c7e464b4ccae64a5a61}
 
 \begin{CompactList}\small\item\em Auxiliary function. \item\end{CompactList}\item 
@@ -63,8 +60,8 @@
 \begin{CompactList}\small\item\em Incremental Bayes rule. \item\end{CompactList}\item 
 \hypertarget{classbdm_1_1ARX_16797df43f85f1ddbe9d64fd6d82c25d}{
-const \hyperlink{classbdm_1_1epdf}{epdf} \& \hyperlink{classbdm_1_1ARX_16797df43f85f1ddbe9d64fd6d82c25d}{\_\-epdf} () const }
+const \hyperlink{classbdm_1_1epdf}{epdf} \& \textbf{\_\-epdf} () const }
 \label{classbdm_1_1ARX_16797df43f85f1ddbe9d64fd6d82c25d}
 
-\begin{CompactList}\small\item\em Returns a reference to the \hyperlink{classbdm_1_1epdf}{epdf} representing posterior density on parameters. \item\end{CompactList}\item 
+\item 
 double \hyperlink{classbdm_1_1ARX_080a7e531e3aa06694112863b15bc6a4}{logpred} (const vec \&dt) const 
 \item 
@@ -74,22 +71,19 @@
 
 \begin{CompactList}\small\item\em Flatten the posterior according to the given \hyperlink{classbdm_1_1BMEF}{BMEF} (of the same type!). \item\end{CompactList}\item 
-\hypertarget{classbdm_1_1ARX_7c8d1fe774fe1da50293d50ad8aef43d}{
-\hyperlink{classbdm_1_1enorm}{enorm}$<$ \hyperlink{classldmat}{ldmat} $>$ $\ast$ \hyperlink{classbdm_1_1ARX_7c8d1fe774fe1da50293d50ad8aef43d}{predictor} (const \hyperlink{classbdm_1_1RV}{RV} \&rv0, const vec \&rgr) const }
-\label{classbdm_1_1ARX_7c8d1fe774fe1da50293d50ad8aef43d}
+\hypertarget{classbdm_1_1ARX_749827323c034f11bec61b6e2fc3d42a}{
+\hyperlink{classbdm_1_1enorm}{enorm}$<$ \hyperlink{classldmat}{ldmat} $>$ $\ast$ \hyperlink{classbdm_1_1ARX_749827323c034f11bec61b6e2fc3d42a}{epredictor} (const vec \&rgr) const }
+\label{classbdm_1_1ARX_749827323c034f11bec61b6e2fc3d42a}
 
 \begin{CompactList}\small\item\em Conditioned version of the predictor. \item\end{CompactList}\item 
-\hypertarget{classbdm_1_1ARX_5b73b70457f49ce4ad8660d729172dfd}{
-\hyperlink{classbdm_1_1enorm}{enorm}$<$ \hyperlink{classldmat}{ldmat} $>$ $\ast$ \hyperlink{classbdm_1_1ARX_5b73b70457f49ce4ad8660d729172dfd}{predictor} (const \hyperlink{classbdm_1_1RV}{RV} \&rv0) const }
-\label{classbdm_1_1ARX_5b73b70457f49ce4ad8660d729172dfd}
-
-\begin{CompactList}\small\item\em Constructs a predictive density (marginal density on data). \item\end{CompactList}\item 
-\hypertarget{classbdm_1_1ARX_02d9e91f21a700947a7b7eec1beed956}{
-\hyperlink{classbdm_1_1mlnorm}{mlnorm}$<$ \hyperlink{classldmat}{ldmat} $>$ $\ast$ \hyperlink{classbdm_1_1ARX_02d9e91f21a700947a7b7eec1beed956}{predictor} (const \hyperlink{classbdm_1_1RV}{RV} \&rv0, const \hyperlink{classbdm_1_1RV}{RV} \&rvc0) const }
-\label{classbdm_1_1ARX_02d9e91f21a700947a7b7eec1beed956}
-
+\hypertarget{classbdm_1_1ARX_4cdf5e2a7d3480ec31f6247ed4289b15}{
+\hyperlink{classbdm_1_1enorm}{enorm}$<$ \hyperlink{classldmat}{ldmat} $>$ $\ast$ \hyperlink{classbdm_1_1ARX_4cdf5e2a7d3480ec31f6247ed4289b15}{epredictor} () const }
+\label{classbdm_1_1ARX_4cdf5e2a7d3480ec31f6247ed4289b15}
+
+\begin{CompactList}\small\item\em Constructs a predictive density $ f(d_{t+1} |d_{t}, \ldots d_{0}) $. \item\end{CompactList}\item 
+\hyperlink{classbdm_1_1mlnorm}{mlnorm}$<$ \hyperlink{classldmat}{ldmat} $>$ $\ast$ \hyperlink{classbdm_1_1ARX_74fe8ae2d88bee8639510fd0eaf73513}{predictor} () const 
 \begin{CompactList}\small\item\em conditional version of the predictor \item\end{CompactList}\item 
-\hypertarget{classbdm_1_1ARX_2ce8c6599497ffb94dfcb66d1fe7aca6}{
-\hyperlink{classbdm_1_1mlstudent}{mlstudent} $\ast$ \textbf{predictor\_\-student} (const \hyperlink{classbdm_1_1RV}{RV} \&rv0, const \hyperlink{classbdm_1_1RV}{RV} \&rvc0) const }
-\label{classbdm_1_1ARX_2ce8c6599497ffb94dfcb66d1fe7aca6}
+\hypertarget{classbdm_1_1ARX_c6a2428a46407fe45b4c7a99069c0801}{
+\hyperlink{classbdm_1_1mlstudent}{mlstudent} $\ast$ \textbf{predictor\_\-student} () const }
+\label{classbdm_1_1ARX_c6a2428a46407fe45b4c7a99069c0801}
 
 \item 
@@ -97,8 +91,16 @@
 \begin{CompactList}\small\item\em Brute force structure estimation. \item\end{CompactList}\item 
 \hypertarget{classbdm_1_1ARX_ab2c55205a324e9d698fbd8ac229ad4f}{
-const \hyperlink{classbdm_1_1egiw}{egiw} $\ast$ \hyperlink{classbdm_1_1ARX_ab2c55205a324e9d698fbd8ac229ad4f}{\_\-e} () const }
+const \hyperlink{classbdm_1_1egiw}{egiw} $\ast$ \textbf{\_\-e} () const }
 \label{classbdm_1_1ARX_ab2c55205a324e9d698fbd8ac229ad4f}
 
-\begin{CompactList}\small\item\em Returns a pointer to the \hyperlink{classbdm_1_1epdf}{epdf} representing posterior density on parameters. Use with care! \item\end{CompactList}\item 
+\item 
+\hypertarget{classbdm_1_1BMEF_5912dbcf28ae711e30b08c2fa766a3e6}{
+\hyperlink{classbdm_1_1BMEF}{BMEF} $\ast$ \hyperlink{classbdm_1_1BMEF_5912dbcf28ae711e30b08c2fa766a3e6}{\_\-copy\_\-} (bool changerv=false)}
+\label{classbdm_1_1BMEF_5912dbcf28ae711e30b08c2fa766a3e6}
+
+\begin{CompactList}\small\item\em Flatten the posterior as if to keep nu0 data. \item\end{CompactList}\end{CompactItemize}
+\begin{Indent}{\bf Mathematical operations}\par
+\begin{CompactItemize}
+\item 
 \hypertarget{classbdm_1_1BM_1dee3fddaf021e62d925289660a707dc}{
 virtual void \hyperlink{classbdm_1_1BM_1dee3fddaf021e62d925289660a707dc}{bayesB} (const mat \&Dt)}
@@ -110,30 +112,30 @@
 \label{classbdm_1_1BM_0e8ebe61fb14990abe1254bd3dda5fae}
 
-\begin{CompactList}\small\item\em Matrix version of logpred. \item\end{CompactList}\item 
-\hypertarget{classbdm_1_1BM_40a3c891996391e3135518053a917793}{
-const \hyperlink{classbdm_1_1RV}{RV} \& \hyperlink{classbdm_1_1BM_40a3c891996391e3135518053a917793}{\_\-rv} () const }
-\label{classbdm_1_1BM_40a3c891996391e3135518053a917793}
-
-\begin{CompactList}\small\item\em access function \item\end{CompactList}\item 
+\begin{CompactList}\small\item\em Matrix version of logpred. \item\end{CompactList}\end{CompactItemize}
+\end{Indent}
+\begin{Indent}{\bf Access to attributes}\par
+\begin{CompactItemize}
+\item 
 \hypertarget{classbdm_1_1BM_ff2d8755ba0b3def927d31305c03b09c}{
-const \hyperlink{classbdm_1_1RV}{RV} \& \hyperlink{classbdm_1_1BM_ff2d8755ba0b3def927d31305c03b09c}{\_\-drv} () const }
+const \hyperlink{classbdm_1_1RV}{RV} \& \textbf{\_\-drv} () const }
 \label{classbdm_1_1BM_ff2d8755ba0b3def927d31305c03b09c}
 
-\begin{CompactList}\small\item\em access function \item\end{CompactList}\item 
+\item 
 \hypertarget{classbdm_1_1BM_f135ae6dce7e9f30c9f88229c7930b96}{
-void \hyperlink{classbdm_1_1BM_f135ae6dce7e9f30c9f88229c7930b96}{set\_\-drv} (const \hyperlink{classbdm_1_1RV}{RV} \&\hyperlink{classbdm_1_1BM_18d6db4af8ee42077741d9e3618153ca}{rv})}
+void \textbf{set\_\-drv} (const \hyperlink{classbdm_1_1RV}{RV} \&rv)}
 \label{classbdm_1_1BM_f135ae6dce7e9f30c9f88229c7930b96}
 
-\begin{CompactList}\small\item\em set drv \item\end{CompactList}\item 
+\item 
 \hypertarget{classbdm_1_1BM_5be65d37dedfe33a3671e7065f523a70}{
-double \hyperlink{classbdm_1_1BM_5be65d37dedfe33a3671e7065f523a70}{\_\-ll} () const }
+double \textbf{\_\-ll} () const }
 \label{classbdm_1_1BM_5be65d37dedfe33a3671e7065f523a70}
 
-\begin{CompactList}\small\item\em access function \item\end{CompactList}\item 
+\item 
 \hypertarget{classbdm_1_1BM_236b3abbcc93594fc97cd86d82c1a83f}{
-void \hyperlink{classbdm_1_1BM_236b3abbcc93594fc97cd86d82c1a83f}{set\_\-evalll} (bool evl0)}
+void \textbf{set\_\-evalll} (bool evl0)}
 \label{classbdm_1_1BM_236b3abbcc93594fc97cd86d82c1a83f}
 
-\begin{CompactList}\small\item\em access function \item\end{CompactList}\end{CompactItemize}
+\end{CompactItemize}
+\end{Indent}
 \subsection*{Protected Attributes}
 \begin{CompactItemize}
@@ -164,9 +166,4 @@
 
 \begin{CompactList}\small\item\em cached value of lognc() in the previous step (used in evaluation of {\tt ll} ) \item\end{CompactList}\item 
-\hypertarget{classbdm_1_1BM_18d6db4af8ee42077741d9e3618153ca}{
-\hyperlink{classbdm_1_1RV}{RV} \hyperlink{classbdm_1_1BM_18d6db4af8ee42077741d9e3618153ca}{rv}}
-\label{classbdm_1_1BM_18d6db4af8ee42077741d9e3618153ca}
-
-\begin{CompactList}\small\item\em Random variable of the posterior. \item\end{CompactList}\item 
 \hypertarget{classbdm_1_1BM_c400357e37d27a4834b2b1d9211009ed}{
 \hyperlink{classbdm_1_1RV}{RV} \hyperlink{classbdm_1_1BM_c400357e37d27a4834b2b1d9211009ed}{drv}}
@@ -185,11 +182,4 @@
 \begin{CompactList}\small\item\em If true, the filter will compute likelihood of the data record and store it in {\tt ll} . Set to false if you want to save computational time. \item\end{CompactList}\end{CompactItemize}
 
-
-\subsection{Detailed Description}
-Linear Autoregressive model with Gaussian noise. 
-
-Regression of the following kind: \[ y_t = \theta_1 \psi_1 + \theta_2 + \psi_2 +\ldots + \theta_n \psi_n + r e_t \] where unknown parameters {\tt rv} are $[\theta r]$, regression vector $\psi=\psi(y_{1:t},u_{1:t})$ is a known function of past outputs and exogeneous variables $u_t$. Distrubances $e_t$ are supposed to be normally distributed: \[ e_t \sim \mathcal{N}(0,1). \]
-
-Extension for time-variant parameters $\theta_t,r_t$ may be achived using exponential forgetting (Kulhavy and Zarrop, 1993). In such a case, the forgetting factor {\tt frg} $\in <0,1>$ should be given in the constructor. Time-invariant parameters are estimated for {\tt frg} = 1. 
 
 \subsection{Member Function Documentation}
@@ -223,5 +213,20 @@
 Reimplemented from \hyperlink{classbdm_1_1BM_50257e0c1e5b5c73153ea6e716ad8ae0}{bdm::BM}.
 
-References bdm::egiw::\_\-nu(), bdm::egiw::\_\-V(), est, bdm::BM::evalll, bdm::BMEF::frg, bdm::BMEF::last\_\-lognc, bdm::egiw::lognc(), nu, ldmat::opupdt(), bdm::egiw::pow(), and V.\hypertarget{classbdm_1_1ARX_16b02ae03316751664c22d59d90c1e34}{
+References bdm::egiw::\_\-nu(), bdm::egiw::\_\-V(), est, bdm::BM::evalll, bdm::BMEF::frg, bdm::BMEF::last\_\-lognc, bdm::egiw::lognc(), nu, ldmat::opupdt(), bdm::egiw::pow(), and V.\hypertarget{classbdm_1_1ARX_74fe8ae2d88bee8639510fd0eaf73513}{
+\index{bdm::ARX@{bdm::ARX}!predictor@{predictor}}
+\index{predictor@{predictor}!bdm::ARX@{bdm::ARX}}
+\subsubsection[predictor]{\setlength{\rightskip}{0pt plus 5cm}{\bf mlnorm}$<$ {\bf ldmat} $>$ $\ast$ bdm::ARX::predictor () const\hspace{0.3cm}{\tt  \mbox{[}virtual\mbox{]}}}}
+\label{classbdm_1_1ARX_74fe8ae2d88bee8639510fd0eaf73513}
+
+
+conditional version of the predictor 
+
+
+
+$<$----------- TODO 
+
+Reimplemented from \hyperlink{classbdm_1_1BM_598b25e3f3d96a5bc00a5faeb5b3c912}{bdm::BM}.
+
+References bdm::epdf::dimension(), est, bdm::egiw::mean\_\-mat(), ldmat::rows(), bdm::mlnorm$<$ sq\_\-T $>$::set\_\-parameters(), and V.\hypertarget{classbdm_1_1ARX_16b02ae03316751664c22d59d90c1e34}{
 \index{bdm::ARX@{bdm::ARX}!structure\_\-est@{structure\_\-est}}
 \index{structure\_\-est@{structure\_\-est}!bdm::ARX@{bdm::ARX}}
@@ -236,5 +241,5 @@
 
 
-References bdm::RV::count(), bdm::egiw\_\-bestbelow(), est, and bdm::egiw::lognc().
+References bdm::epdf::dimension(), bdm::egiw\_\-bestbelow(), est, and bdm::egiw::lognc().
 
 The documentation for this class was generated from the following files:\begin{CompactItemize}
