root/doc/latex/classKalmanCh.tex @ 157

\section{KalmanCh Class Reference}
\label{classKalmanCh}\index{KalmanCh@{KalmanCh}}
\doxyref{Kalman}{p.}{classKalman} filter in square root form.  


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

Inheritance diagram for KalmanCh:\nopagebreak
\begin{figure}[H]
\begin{center}
\leavevmode
\includegraphics[width=95pt]{classKalmanCh__inherit__graph}
\end{center}
\end{figure}
Collaboration diagram for KalmanCh:\nopagebreak
\begin{figure}[H]
\begin{center}
\leavevmode
\includegraphics[width=400pt]{classKalmanCh__coll__graph}
\end{center}
\end{figure}
\subsection*{Public Member Functions}
\begin{CompactItemize}
\item 
{\bf KalmanCh} ({\bf RV} rvx0, {\bf RV} rvy0, {\bf RV} rvu0)\label{classKalmanCh_d11f110cccaa66177514632d37b086bb}

\begin{CompactList}\small\item\em Default constructor. \item\end{CompactList}\item 
void {\bf set\_\-parameters} (const mat \&A0, const mat \&B0, const mat \&C0, const mat \&D0, const {\bf chmat} \&R0, const {\bf chmat} \&Q0)\label{classKalmanCh_92fb227287af05c9f0078d523c7c9793}

\begin{CompactList}\small\item\em Set parameters with check of relevance. \item\end{CompactList}\item 
void {\bf set\_\-est} (const vec \&mu0, const {\bf chmat} \&P0)\label{classKalmanCh_b261b20f6210d4c85131d33302df0adc}

\begin{CompactList}\small\item\em Set estimate values, used e.g. in initialization. \item\end{CompactList}\item 
void {\bf bayes} (const vec \&dt)
\begin{CompactList}\small\item\em Here dt = [yt;ut] of appropriate dimensions. \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 access function \item\end{CompactList}\item 
mat \& {\bf \_\-\_\-K} ()\label{classKalman_980fcd41c6c548c5da7b8b67c8e6da79}

\begin{CompactList}\small\item\em access function \item\end{CompactList}\item 
vec {\bf \_\-dP} ()\label{classKalman_ac9540f3850b74d89a5fe4db6fc358ce}

\begin{CompactList}\small\item\em access function \item\end{CompactList}\item 
const {\bf RV} \& {\bf \_\-rv} () const \label{classBM_126bd2595c48e311fc2a7ab72876092a}

\begin{CompactList}\small\item\em access function \item\end{CompactList}\item 
double {\bf \_\-ll} () const \label{classBM_87f4a547d2c29180be88175e5eab9c88}

\begin{CompactList}\small\item\em access function \item\end{CompactList}\end{CompactItemize}
\subsection*{Protected Attributes}
\begin{CompactItemize}
\item 
mat {\bf preA}\label{classKalmanCh_94ee9da75b0e0f632e4a354988ca3798}

\begin{CompactList}\small\item\em pre array (triangular matrix) \item\end{CompactList}\item 
mat {\bf postA}\label{classKalmanCh_0d31a26dc72b5846cfe5af3ccb63ac87}

\begin{CompactList}\small\item\em post array (triangular matrix) \item\end{CompactList}\item 
{\bf RV} {\bf rvy}\label{classKalman_7501230c2fafa3655887d2da23b3184c}

\begin{CompactList}\small\item\em Indetifier of output rv. \item\end{CompactList}\item 
{\bf RV} {\bf rvu}\label{classKalman_44a16ffd5ac1e6e39bae34fea9e1e498}

\begin{CompactList}\small\item\em Indetifier of exogeneous rv. \item\end{CompactList}\item 
int {\bf dimx}\label{classKalman_39c8c403b46fa3b8c7da77cb2e3729eb}

\begin{CompactList}\small\item\em cache of rv.count() \item\end{CompactList}\item 
int {\bf dimy}\label{classKalman_ba17b956df1e38b31fbbc299c8213b6a}

\begin{CompactList}\small\item\em cache of rvy.count() \item\end{CompactList}\item 
int {\bf dimu}\label{classKalman_b0153795a1444b6968a86409c778d9ce}

\begin{CompactList}\small\item\em cache of rvu.count() \item\end{CompactList}\item 
mat {\bf A}\label{classKalman_5e02efe86ee91e9c74b93b425fe060b9}

\begin{CompactList}\small\item\em Matrix A. \item\end{CompactList}\item 
mat {\bf B}\label{classKalman_dc87704284a6c0bca13bf51f4345a50a}

\begin{CompactList}\small\item\em Matrix B. \item\end{CompactList}\item 
mat {\bf C}\label{classKalman_86a805cd6515872d1132ad0d6eb5dc13}

\begin{CompactList}\small\item\em Matrix C. \item\end{CompactList}\item 
mat {\bf D}\label{classKalman_d69f774ba3335c970c1c5b1d182f4dd1}

\begin{CompactList}\small\item\em Matrix D. \item\end{CompactList}\item 
{\bf chmat} {\bf Q}\label{classKalman_9b69015c800eb93f3ee49da23a6f55d9}

\begin{CompactList}\small\item\em Matrix Q in square-root form. \item\end{CompactList}\item 
{\bf chmat} {\bf R}\label{classKalman_11d171dc0e0ab111c56a70f98b97b3ec}

\begin{CompactList}\small\item\em Matrix R in square-root form. \item\end{CompactList}\item 
{\bf enorm}$<$ {\bf chmat} $>$ {\bf est}\label{classKalman_5568c74bac67ae6d3b1061dba60c9424}

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

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

\begin{CompactList}\small\item\em placeholder for \doxyref{Kalman}{p.}{classKalman} gain \item\end{CompactList}\item 
vec \& {\bf \_\-yp}\label{classKalman_764bbc95238eda11fc81c5ebd0b1dcfd}

\begin{CompactList}\small\item\em cache of fy.mu \item\end{CompactList}\item 
{\bf chmat} \& {\bf \_\-Ry}\label{classKalman_45c9f928d2d62e0c884900fb3380f904}

\begin{CompactList}\small\item\em cache of fy.R \item\end{CompactList}\item 
vec \& {\bf \_\-mu}\label{classKalman_fe803a81d2d847b0b1db3c6b29c18061}

\begin{CompactList}\small\item\em cache of est.mu \item\end{CompactList}\item 
{\bf chmat} \& {\bf \_\-P}\label{classKalman_9fb808cc94a4c2652e1fb93be9bb7dcf}

\begin{CompactList}\small\item\em cache of est.R \item\end{CompactList}\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}
\doxyref{Kalman}{p.}{classKalman} filter in square root form. 

\subsection{Member Function Documentation}
\index{KalmanCh@{KalmanCh}!bayes@{bayes}}
\index{bayes@{bayes}!KalmanCh@{KalmanCh}}
\subsubsection[bayes]{\setlength{\rightskip}{0pt plus 5cm}void KalmanCh::bayes (const vec \& {\em dt})\hspace{0.3cm}{\tt  [virtual]}}\label{classKalmanCh_cca758192846940409822b9bd778d4e1}


Here dt = [yt;ut] of appropriate dimensions. 

The following equality hold::\[ \left[\begin{array}{cc} R^{0.5}\\ P_{t|t-1}^{0.5}C' & P_{t|t-1}^{0.5}CA'\\ & Q^{0.5}\end{array}\right]<\mathrm{orth.oper.}>=\left[\begin{array}{cc} R_{y}^{0.5} & KA'\\ & P_{t+1|t}^{0.5}\\ \\\end{array}\right]\]

Thus this object evaluates only predictors! Not filtering densities. 

Reimplemented from {\bf Kalman$<$ chmat $>$} \doxyref{}{p.}{classKalman_7750ffd73f261828a32c18aaeb65c75c}.

Reimplemented in {\bf EKFCh} \doxyref{}{p.}{classEKFCh_96f6edda324a0b7ef8b4e86cc7af60c1}.

References chmat::\_\-Ch(), Kalman$<$ chmat $>$::\_\-K, Kalman$<$ chmat $>$::\_\-mu, Kalman$<$ chmat $>$::\_\-P, Kalman$<$ chmat $>$::\_\-Ry, Kalman$<$ chmat $>$::\_\-yp, Kalman$<$ chmat $>$::A, Kalman$<$ chmat $>$::B, Kalman$<$ chmat $>$::C, Kalman$<$ chmat $>$::D, Kalman$<$ chmat $>$::dimu, Kalman$<$ chmat $>$::dimx, Kalman$<$ chmat $>$::dimy, BM::evalll, enorm$<$ sq\_\-T $>$::evalpdflog(), Kalman$<$ chmat $>$::fy, BM::ll, postA, preA, and chmat::to\_\-mat().

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