root/doc/latex/classKalmanFull.tex @ 30

\section{KalmanFull Class Reference}
\label{classKalmanFull}\index{KalmanFull@{KalmanFull}}
Basic \doxyref{Kalman}{p.}{classKalman} filter with full matrices (education purpose only)! Will be deleted soon!  


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

Inheritance diagram for KalmanFull:\nopagebreak
\begin{figure}[H]
\begin{center}
\leavevmode
\includegraphics[width=56pt]{classKalmanFull__inherit__graph}
\end{center}
\end{figure}
Collaboration diagram for KalmanFull:\nopagebreak
\begin{figure}[H]
\begin{center}
\leavevmode
\includegraphics[width=56pt]{classKalmanFull__coll__graph}
\end{center}
\end{figure}
\subsection*{Public Member Functions}
\begin{CompactItemize}
\item 
{\bf KalmanFull} (mat A, mat B, mat C, mat D, mat R, mat Q, mat P0, vec mu0)\label{classKalmanFull_7197ab6e7380790006394eabd3b97043}

\begin{CompactList}\small\item\em Full constructor. \item\end{CompactList}\item 
void {\bf bayes} (const vec \&dt, bool {\bf evalll}=true)\label{classKalmanFull_048b13739b94c331cda08249b278552b}

\begin{CompactList}\small\item\em Here dt = [yt;ut] of appropriate dimensions. \item\end{CompactList}\item 
virtual void {\bf bayes} (const vec \&dt)=0
\begin{CompactList}\small\item\em Incremental Bayes rule. \item\end{CompactList}\item 
void {\bf bayes} (mat Dt)\label{classBM_87b07867fd4c133aa89a18543f68d9f9}

\begin{CompactList}\small\item\em Batch Bayes rule (columns of Dt are observations). \item\end{CompactList}\item 
{\bf epdf} $\ast$ {\bf \_\-epdf} ()\label{classBM_a5b8f6c8a872738cfaa30ab010e8c077}

\begin{CompactList}\small\item\em Returns a pointer to the \doxyref{epdf}{p.}{classepdf} representing posterior density on parameters. Use with care! \item\end{CompactList}\end{CompactItemize}
\subsection*{Public Attributes}
\begin{CompactItemize}
\item 
vec {\bf mu}\label{classKalmanFull_fb5aec635e2720cc5ac31bc01c18a68a}

\begin{CompactList}\small\item\em Mean value of the posterior density. \item\end{CompactList}\item 
mat {\bf P}\label{classKalmanFull_b75dc059e84fa8ffc076203b30f926cc}

\begin{CompactList}\small\item\em Variance of the posterior density. \item\end{CompactList}\item 
double {\bf ll}\label{classBM_5623fef6572a08c2b53b8c87b82dc979}

\begin{CompactList}\small\item\em Logarithm of marginalized data likelihood. \item\end{CompactList}\item 
bool {\bf evalll}\label{classBM_bf6fb59b30141074f8ee1e2f43d03129}

\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 time. \item\end{CompactList}\end{CompactItemize}
\subsection*{Friends}
\begin{CompactItemize}
\item 
std::ostream \& \textbf{operator$<$$<$} (std::ostream \&os, const {\bf KalmanFull} \&kf)\label{classKalmanFull_86ba216243ed95bb46d80d88775d16af}

\end{CompactItemize}


\subsection{Detailed Description}
Basic \doxyref{Kalman}{p.}{classKalman} filter with full matrices (education purpose only)! Will be deleted soon! 

\subsection{Member Function Documentation}
\index{KalmanFull@{KalmanFull}!bayes@{bayes}}
\index{bayes@{bayes}!KalmanFull@{KalmanFull}}
\subsubsection{\setlength{\rightskip}{0pt plus 5cm}virtual void BM::bayes (const vec \& {\em dt})\hspace{0.3cm}{\tt  [pure virtual, inherited]}}\label{classBM_a892eff438aab2dd1a9e2efcb7fb5bdf}


Incremental Bayes rule. 

\begin{Desc}
\item[Parameters:]
\begin{description}
\item[{\em dt}]vector of input data \end{description}
\end{Desc}


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