\section{Kalman$<$ sq\_\-T $>$ Class Template Reference}
\label{classKalman}\index{Kalman@{Kalman}}
\doxyref{Kalman}{p.}{classKalman} filter with covariance matrices in square root form.  


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

Inheritance diagram for Kalman$<$ sq\_\-T $>$:\nopagebreak
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Collaboration diagram for Kalman$<$ sq\_\-T $>$:\nopagebreak
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\subsection*{Public Member Functions}
\begin{CompactItemize}
\item 
{\bf Kalman} (int dimx, int dimu, int dimy)\label{classKalman_96958a5ebfa966d892137987f265083a}

\begin{CompactList}\small\item\em Default constructor. \item\end{CompactList}\item 
{\bf Kalman} (mat A0, mat B0, mat C0, mat D0, sq\_\-T R0, sq\_\-T Q0, sq\_\-T P0, vec mu0)\label{classKalman_83118f4bd2ecbc70b03cfd573088ed6f}

\begin{CompactList}\small\item\em Full constructor. \item\end{CompactList}\item 
void {\bf bayes} (const vec \&dt, bool {\bf evalll}=true)\label{classKalman_e945d9205ca14acbd83ba80ea6f72b8e}

\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{classKalman_3063a3f58a74cea672ae889971012eed}

\begin{CompactList}\small\item\em Mean value of the posterior density. \item\end{CompactList}\item 
sq\_\-T {\bf P}\label{classKalman_188cd5ac1c9e496b1a371eb7c57c97d3}

\begin{CompactList}\small\item\em Mean value 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*{Protected Attributes}
\begin{CompactItemize}
\item 
int \textbf{dimx}\label{classKalman_39c8c403b46fa3b8c7da77cb2e3729eb}

\item 
int \textbf{dimy}\label{classKalman_ba17b956df1e38b31fbbc299c8213b6a}

\item 
int \textbf{dimu}\label{classKalman_b0153795a1444b6968a86409c778d9ce}

\item 
mat \textbf{A}\label{classKalman_5e02efe86ee91e9c74b93b425fe060b9}

\item 
mat \textbf{B}\label{classKalman_dc87704284a6c0bca13bf51f4345a50a}

\item 
mat \textbf{C}\label{classKalman_86a805cd6515872d1132ad0d6eb5dc13}

\item 
mat \textbf{D}\label{classKalman_d69f774ba3335c970c1c5b1d182f4dd1}

\item 
sq\_\-T \textbf{R}\label{classKalman_11d171dc0e0ab111c56a70f98b97b3ec}

\item 
sq\_\-T \textbf{Q}\label{classKalman_9b69015c800eb93f3ee49da23a6f55d9}

\item 
mat \textbf{\_\-K}\label{classKalman_d422f51467c7a06174af2476d2826132}

\item 
vec \textbf{\_\-yp}\label{classKalman_30b7461989185d3d02cf42b8e2a37649}

\item 
sq\_\-T \textbf{\_\-Ry}\label{classKalman_477dca07d91ea1a1f41d51bb0229934f}

\item 
sq\_\-T \textbf{\_\-iRy}\label{classKalman_15f1a793210750a7e4642fcd948b24c5}

\end{CompactItemize}
\subsection*{Friends}
\begin{CompactItemize}
\item 
std::ostream \& \textbf{operator$<$$<$} (std::ostream \&os, const {\bf KalmanFull} \&kf)\label{classKalman_86ba216243ed95bb46d80d88775d16af}

\end{CompactItemize}


\subsection{Detailed Description}
\subsubsection*{template$<$class sq\_\-T$>$ class Kalman$<$ sq\_\-T $>$}

\doxyref{Kalman}{p.}{classKalman} filter with covariance matrices in square root form. 

\subsection{Member Function Documentation}
\index{Kalman@{Kalman}!bayes@{bayes}}
\index{bayes@{bayes}!Kalman@{Kalman}}
\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 file:\begin{CompactItemize}
\item 
work/mixpp/bdm/estim/{\bf libKF.h}\end{CompactItemize}