\section{EKF$<$ sq\_\-T $>$ Class Template Reference}
\label{classEKF}\index{EKF@{EKF}}
Extended \doxyref{Kalman}{p.}{classKalman} Filter.  


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

Inheritance diagram for EKF$<$ sq\_\-T $>$:\nopagebreak
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Collaboration diagram for EKF$<$ sq\_\-T $>$:\nopagebreak
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\subsection*{Public Member Functions}
\begin{CompactItemize}
\item 
{\bf EKF} ({\bf RV} rvx, {\bf RV} rvy, {\bf RV} rvu)\label{classEKF_ea4f3254cacf0a92d2a820b1201d049e}

\begin{CompactList}\small\item\em Default constructor. \item\end{CompactList}\item 
void \textbf{set\_\-parameters} ({\bf diffbifn} $\ast$pfxu, {\bf diffbifn} $\ast$phxu, const sq\_\-T Q0, const sq\_\-T R0)\label{classEKF_28d058ae4d24d992d2f055419a06ee66}

\item 
void {\bf bayes} (const vec \&dt)\label{classEKF_c79c62c9b3e0b56b3aaa1b6f1d9a7af7}

\begin{CompactList}\small\item\em Here dt = [yt;ut] of appropriate dimensions. \item\end{CompactList}\item 
void {\bf set\_\-parameters} (const mat \&A0, const mat \&B0, const mat \&C0, const mat \&D0, const ldmat \&R0, const ldmat \&Q0)\label{classKalman_239b28a0380946f5749b2f8d2807f93a}

\begin{CompactList}\small\item\em Set parameters with check of relevance. \item\end{CompactList}\item 
void {\bf set\_\-est} (const vec \&mu0, const ldmat \&P0)\label{classKalman_80bcf29466d9a9dd2b8f74699807d0c0}

\begin{CompactList}\small\item\em Set estimate values, used e.g. in initialization. \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} \& {\bf \_\-epdf} ()\label{classKalman_a213c57aef55b2645e550bed81cfc0d4}

\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*{Protected Attributes}
\begin{CompactItemize}
\item 
{\bf RV} \textbf{rvy}\label{classKalman_7501230c2fafa3655887d2da23b3184c}

\item 
{\bf RV} \textbf{rvu}\label{classKalman_44a16ffd5ac1e6e39bae34fea9e1e498}

\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 
ldmat \textbf{R}\label{classKalman_11d171dc0e0ab111c56a70f98b97b3ec}

\item 
ldmat \textbf{Q}\label{classKalman_9b69015c800eb93f3ee49da23a6f55d9}

\item 
{\bf enorm}$<$ ldmat $>$ {\bf est}\label{classKalman_5568c74bac67ae6d3b1061dba60c9424}

\begin{CompactList}\small\item\em posterior density on \$x\_\-t\$ \item\end{CompactList}\item 
{\bf enorm}$<$ ldmat $>$ {\bf fy}\label{classKalman_e580ab06483952bd03f2e651763e184f}

\begin{CompactList}\small\item\em preditive density on \$y\_\-t\$ \item\end{CompactList}\item 
mat \textbf{\_\-K}\label{classKalman_d422f51467c7a06174af2476d2826132}

\item 
vec $\ast$ \textbf{\_\-yp}\label{classKalman_5188eb0329f8561f0b357af329769bf8}

\item 
ldmat $\ast$ \textbf{\_\-Ry}\label{classKalman_e17dd745daa8a958035a334a56fa4674}

\item 
ldmat $\ast$ \textbf{\_\-iRy}\label{classKalman_fbbdf31365f5a5674099599200ea193b}

\item 
vec $\ast$ \textbf{\_\-mu}\label{classKalman_d1f669b5b3421a070cc75d77b55ba734}

\item 
ldmat $\ast$ \textbf{\_\-P}\label{classKalman_b3388218567128a797e69b109138271d}

\item 
ldmat $\ast$ \textbf{\_\-iP}\label{classKalman_b8bb7f870d69993493ba67ce40e7c3e9}

\item 
{\bf RV} {\bf rv}\label{classBM_af00f0612fabe66241dd507188cdbf88}

\begin{CompactList}\small\item\em Random variable of the posterior. \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{Detailed Description}
\subsubsection*{template$<$class sq\_\-T$>$ class EKF$<$ sq\_\-T $>$}

Extended \doxyref{Kalman}{p.}{classKalman} Filter. 

An approximation of the exact Bayesian filter with Gaussian noices and non-linear evolutions of their mean. 

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