\hypertarget{classbdm_1_1EKFfull}{
\section{bdm::EKFfull Class Reference}
\label{classbdm_1_1EKFfull}\index{bdm::EKFfull@{bdm::EKFfull}}
}
{\tt \#include $<$libKF.h$>$}

Inheritance diagram for bdm::EKFfull:\nopagebreak
\begin{figure}[H]
\begin{center}
\leavevmode
\includegraphics[width=113pt]{classbdm_1_1EKFfull__inherit__graph}
\end{center}
\end{figure}


\subsection{Detailed Description}
Extended \hyperlink{classbdm_1_1Kalman}{Kalman} Filter in full matrices. 

An approximation of the exact Bayesian filter with Gaussian noices and non-linear evolutions of their mean. \subsection*{Public Member Functions}
\begin{CompactItemize}
\item 
\hypertarget{classbdm_1_1EKFfull_6939c345389abb8b2481457b4cfe1165}{
\hyperlink{classbdm_1_1EKFfull_6939c345389abb8b2481457b4cfe1165}{EKFfull} ()}
\label{classbdm_1_1EKFfull_6939c345389abb8b2481457b4cfe1165}

\begin{CompactList}\small\item\em Default constructor. \item\end{CompactList}\item 
\hypertarget{classbdm_1_1EKFfull_78748da361ba61fef162b0d8956d1743}{
void \hyperlink{classbdm_1_1EKFfull_78748da361ba61fef162b0d8956d1743}{set\_\-parameters} (\hyperlink{classbdm_1_1diffbifn}{diffbifn} $\ast$pfxu, \hyperlink{classbdm_1_1diffbifn}{diffbifn} $\ast$phxu, const mat Q0, const mat R0)}
\label{classbdm_1_1EKFfull_78748da361ba61fef162b0d8956d1743}

\begin{CompactList}\small\item\em Set nonlinear functions for mean values and covariance matrices. \item\end{CompactList}\item 
\hypertarget{classbdm_1_1EKFfull_f149ae8e9ce14d9931a7bb2850736699}{
void \hyperlink{classbdm_1_1EKFfull_f149ae8e9ce14d9931a7bb2850736699}{bayes} (const vec \&dt)}
\label{classbdm_1_1EKFfull_f149ae8e9ce14d9931a7bb2850736699}

\begin{CompactList}\small\item\em Here dt = \mbox{[}yt;ut\mbox{]} of appropriate dimensions. \item\end{CompactList}\item 
\hypertarget{classbdm_1_1EKFfull_7562b3d3c17241dab3baf70258742eb2}{
void \hyperlink{classbdm_1_1EKFfull_7562b3d3c17241dab3baf70258742eb2}{set\_\-est} (vec mu0, mat P0)}
\label{classbdm_1_1EKFfull_7562b3d3c17241dab3baf70258742eb2}

\begin{CompactList}\small\item\em set estimates \item\end{CompactList}\item 
\hypertarget{classbdm_1_1EKFfull_6ccc4fa7da522d1c2257156f72291a8a}{
const \hyperlink{classbdm_1_1epdf}{epdf} \& \hyperlink{classbdm_1_1EKFfull_6ccc4fa7da522d1c2257156f72291a8a}{\_\-epdf} () const }
\label{classbdm_1_1EKFfull_6ccc4fa7da522d1c2257156f72291a8a}

\begin{CompactList}\small\item\em dummy! \item\end{CompactList}\item 
\hypertarget{classbdm_1_1EKFfull_3d0e427d4d2fb7ac20358ce629f5d510}{
const \hyperlink{classbdm_1_1enorm}{enorm}$<$ \hyperlink{classfsqmat}{fsqmat} $>$ $\ast$ \textbf{\_\-e} () const }
\label{classbdm_1_1EKFfull_3d0e427d4d2fb7ac20358ce629f5d510}

\item 
\hypertarget{classbdm_1_1EKFfull_d4f57cb8af64b06c530f528c32596d4d}{
const mat \textbf{\_\-R} ()}
\label{classbdm_1_1EKFfull_d4f57cb8af64b06c530f528c32596d4d}

\end{CompactItemize}
\begin{Indent}{\bf Constructors}\par
\begin{CompactItemize}
\item 
virtual \hyperlink{classbdm_1_1BM}{BM} $\ast$ \hyperlink{classbdm_1_1BM_c0f027ff91d8459937c6f60ff8e553ff}{\_\-copy\_\-} ()
\end{CompactItemize}
\end{Indent}
\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)}
\label{classbdm_1_1BM_1dee3fddaf021e62d925289660a707dc}

\begin{CompactList}\small\item\em Batch Bayes rule (columns of Dt are observations). \item\end{CompactList}\item 
virtual double \hyperlink{classbdm_1_1BM_50257e0c1e5b5c73153ea6e716ad8ae0}{logpred} (const vec \&dt) const 
\item 
\hypertarget{classbdm_1_1BM_0e8ebe61fb14990abe1254bd3dda5fae}{
vec \hyperlink{classbdm_1_1BM_0e8ebe61fb14990abe1254bd3dda5fae}{logpred\_\-m} (const mat \&dt) const }
\label{classbdm_1_1BM_0e8ebe61fb14990abe1254bd3dda5fae}

\begin{CompactList}\small\item\em Matrix version of logpred. \item\end{CompactList}\item 
\hypertarget{classbdm_1_1BM_688d7a2aced1e06aa1c468d73a9e5eba}{
virtual \hyperlink{classbdm_1_1epdf}{epdf} $\ast$ \hyperlink{classbdm_1_1BM_688d7a2aced1e06aa1c468d73a9e5eba}{epredictor} () const }
\label{classbdm_1_1BM_688d7a2aced1e06aa1c468d73a9e5eba}

\begin{CompactList}\small\item\em Constructs a predictive density $ f(d_{t+1} |d_{t}, \ldots d_{0}) $. \item\end{CompactList}\item 
\hypertarget{classbdm_1_1BM_598b25e3f3d96a5bc00a5faeb5b3c912}{
virtual \hyperlink{classbdm_1_1mpdf}{mpdf} $\ast$ \hyperlink{classbdm_1_1BM_598b25e3f3d96a5bc00a5faeb5b3c912}{predictor} () const }
\label{classbdm_1_1BM_598b25e3f3d96a5bc00a5faeb5b3c912}

\begin{CompactList}\small\item\em Constructs a conditional density 1-step ahead predictor. \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} \& \textbf{\_\-drv} () const }
\label{classbdm_1_1BM_ff2d8755ba0b3def927d31305c03b09c}

\item 
\hypertarget{classbdm_1_1BM_f135ae6dce7e9f30c9f88229c7930b96}{
void \textbf{set\_\-drv} (const \hyperlink{classbdm_1_1RV}{RV} \&rv)}
\label{classbdm_1_1BM_f135ae6dce7e9f30c9f88229c7930b96}

\item 
\hypertarget{classbdm_1_1BM_5be65d37dedfe33a3671e7065f523a70}{
double \textbf{\_\-ll} () const }
\label{classbdm_1_1BM_5be65d37dedfe33a3671e7065f523a70}

\item 
\hypertarget{classbdm_1_1BM_236b3abbcc93594fc97cd86d82c1a83f}{
void \textbf{set\_\-evalll} (bool evl0)}
\label{classbdm_1_1BM_236b3abbcc93594fc97cd86d82c1a83f}

\end{CompactItemize}
\end{Indent}
\subsection*{Public Attributes}
\begin{CompactItemize}
\item 
\hypertarget{classbdm_1_1KalmanFull_2defb75e58892615c5f95fd844f3a666}{
vec \hyperlink{classbdm_1_1KalmanFull_2defb75e58892615c5f95fd844f3a666}{mu}}
\label{classbdm_1_1KalmanFull_2defb75e58892615c5f95fd844f3a666}

\begin{CompactList}\small\item\em Mean value of the posterior density. \item\end{CompactList}\item 
\hypertarget{classbdm_1_1KalmanFull_acacd228e100c3e937de575ad2d7cd9c}{
mat \hyperlink{classbdm_1_1KalmanFull_acacd228e100c3e937de575ad2d7cd9c}{P}}
\label{classbdm_1_1KalmanFull_acacd228e100c3e937de575ad2d7cd9c}

\begin{CompactList}\small\item\em Variance of the posterior density. \item\end{CompactList}\item 
\hypertarget{classbdm_1_1KalmanFull_0dba34bfba2aedd8c488692bcd14869b}{
bool \textbf{evalll}}
\label{classbdm_1_1KalmanFull_0dba34bfba2aedd8c488692bcd14869b}

\item 
\hypertarget{classbdm_1_1KalmanFull_363ade67bd5a06c6a45c41e4d8afe11e}{
double \textbf{ll}}
\label{classbdm_1_1KalmanFull_363ade67bd5a06c6a45c41e4d8afe11e}

\end{CompactItemize}
\subsection*{Protected Attributes}
\begin{CompactItemize}
\item 
\hypertarget{classbdm_1_1KalmanFull_427886a66cde0354e041ddef5aa60eab}{
int \textbf{dimx}}
\label{classbdm_1_1KalmanFull_427886a66cde0354e041ddef5aa60eab}

\item 
\hypertarget{classbdm_1_1KalmanFull_2b0399b8904ccb81c2098cc3cc85ff8f}{
int \textbf{dimy}}
\label{classbdm_1_1KalmanFull_2b0399b8904ccb81c2098cc3cc85ff8f}

\item 
\hypertarget{classbdm_1_1KalmanFull_8e886b5d535ba7f9a39e66be34116788}{
int \textbf{dimu}}
\label{classbdm_1_1KalmanFull_8e886b5d535ba7f9a39e66be34116788}

\item 
\hypertarget{classbdm_1_1KalmanFull_a24914cfc0297b9f3885df86e5011733}{
mat \textbf{A}}
\label{classbdm_1_1KalmanFull_a24914cfc0297b9f3885df86e5011733}

\item 
\hypertarget{classbdm_1_1KalmanFull_ef28133db32cc60b710925266c37376d}{
mat \textbf{B}}
\label{classbdm_1_1KalmanFull_ef28133db32cc60b710925266c37376d}

\item 
\hypertarget{classbdm_1_1KalmanFull_89ed156e063e19b32df2218bfaef42cf}{
mat \textbf{C}}
\label{classbdm_1_1KalmanFull_89ed156e063e19b32df2218bfaef42cf}

\item 
\hypertarget{classbdm_1_1KalmanFull_74e9f43b5b4d4a5e012e6178542d3e8f}{
mat \textbf{D}}
\label{classbdm_1_1KalmanFull_74e9f43b5b4d4a5e012e6178542d3e8f}

\item 
\hypertarget{classbdm_1_1KalmanFull_5c1fc8685511d21ba0e1688452105b7c}{
mat \textbf{R}}
\label{classbdm_1_1KalmanFull_5c1fc8685511d21ba0e1688452105b7c}

\item 
\hypertarget{classbdm_1_1KalmanFull_17d9a3316ecf81c149c2c1affb11af58}{
mat \textbf{Q}}
\label{classbdm_1_1KalmanFull_17d9a3316ecf81c149c2c1affb11af58}

\item 
\hypertarget{classbdm_1_1KalmanFull_f7fc60eca2893328d42f92246526d4b9}{
mat \textbf{\_\-Pp}}
\label{classbdm_1_1KalmanFull_f7fc60eca2893328d42f92246526d4b9}

\item 
\hypertarget{classbdm_1_1KalmanFull_b85742b33f95077f360a03ca2de05261}{
mat \textbf{\_\-Ry}}
\label{classbdm_1_1KalmanFull_b85742b33f95077f360a03ca2de05261}

\item 
\hypertarget{classbdm_1_1KalmanFull_09472aa8c06e79944d7637b70bf4e401}{
mat \textbf{\_\-iRy}}
\label{classbdm_1_1KalmanFull_09472aa8c06e79944d7637b70bf4e401}

\item 
\hypertarget{classbdm_1_1KalmanFull_7455b5deee5f14d978c82c5cc9357e29}{
mat \textbf{\_\-K}}
\label{classbdm_1_1KalmanFull_7455b5deee5f14d978c82c5cc9357e29}

\item 
\hypertarget{classbdm_1_1BM_c400357e37d27a4834b2b1d9211009ed}{
\hyperlink{classbdm_1_1RV}{RV} \hyperlink{classbdm_1_1BM_c400357e37d27a4834b2b1d9211009ed}{drv}}
\label{classbdm_1_1BM_c400357e37d27a4834b2b1d9211009ed}

\begin{CompactList}\small\item\em Random variable of the data (optional). \item\end{CompactList}\item 
\hypertarget{classbdm_1_1BM_4064b6559d962633e4372b12f4cd204a}{
double \hyperlink{classbdm_1_1BM_4064b6559d962633e4372b12f4cd204a}{ll}}
\label{classbdm_1_1BM_4064b6559d962633e4372b12f4cd204a}

\begin{CompactList}\small\item\em Logarithm of marginalized data likelihood. \item\end{CompactList}\item 
\hypertarget{classbdm_1_1BM_faff0ad12556fe7dc0e2807d4fd938ee}{
bool \hyperlink{classbdm_1_1BM_faff0ad12556fe7dc0e2807d4fd938ee}{evalll}}
\label{classbdm_1_1BM_faff0ad12556fe7dc0e2807d4fd938ee}

\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*{Friends}
\begin{CompactItemize}
\item 
\hypertarget{classbdm_1_1KalmanFull_86ba216243ed95bb46d80d88775d16af}{
std::ostream \& \hyperlink{classbdm_1_1KalmanFull_86ba216243ed95bb46d80d88775d16af}{operator$<$$<$} (std::ostream \&os, const \hyperlink{classbdm_1_1KalmanFull}{KalmanFull} \&kf)}
\label{classbdm_1_1KalmanFull_86ba216243ed95bb46d80d88775d16af}

\begin{CompactList}\small\item\em print elements of KF \item\end{CompactList}\end{CompactItemize}


\subsection{Member Function Documentation}
\hypertarget{classbdm_1_1BM_c0f027ff91d8459937c6f60ff8e553ff}{
\index{bdm::EKFfull@{bdm::EKFfull}!\_\-copy\_\-@{\_\-copy\_\-}}
\index{\_\-copy\_\-@{\_\-copy\_\-}!bdm::EKFfull@{bdm::EKFfull}}
\subsubsection[\_\-copy\_\-]{\setlength{\rightskip}{0pt plus 5cm}virtual {\bf BM}$\ast$ bdm::BM::\_\-copy\_\- ()\hspace{0.3cm}{\tt  \mbox{[}inline, virtual, inherited\mbox{]}}}}
\label{classbdm_1_1BM_c0f027ff91d8459937c6f60ff8e553ff}


Copy function required in vectors, Arrays of \hyperlink{classbdm_1_1BM}{BM} etc. Have to be DELETED manually! Prototype: 

\begin{Code}\begin{verbatim} BM* _copy_(){return new BM(*this);} 
\end{verbatim}
\end{Code}

 

Reimplemented in \hyperlink{classbdm_1_1ARX_60c40b5c6abc4c7e464b4ccae64a5a61}{bdm::ARX}.\hypertarget{classbdm_1_1BM_50257e0c1e5b5c73153ea6e716ad8ae0}{
\index{bdm::EKFfull@{bdm::EKFfull}!logpred@{logpred}}
\index{logpred@{logpred}!bdm::EKFfull@{bdm::EKFfull}}
\subsubsection[logpred]{\setlength{\rightskip}{0pt plus 5cm}virtual double bdm::BM::logpred (const vec \& {\em dt}) const\hspace{0.3cm}{\tt  \mbox{[}inline, virtual, inherited\mbox{]}}}}
\label{classbdm_1_1BM_50257e0c1e5b5c73153ea6e716ad8ae0}


Evaluates predictive log-likelihood of the given data record I.e. marginal likelihood of the data with the posterior integrated out. 

Reimplemented in \hyperlink{classbdm_1_1ARX_080a7e531e3aa06694112863b15bc6a4}{bdm::ARX}, \hyperlink{classbdm_1_1MixEF_da724da464a75e07521941e430929efa}{bdm::MixEF}, and \hyperlink{classbdm_1_1multiBM_e157b607c1e3fa91d42aeea44458e2bf}{bdm::multiBM}.

Referenced by bdm::BM::logpred\_\-m().

The documentation for this class was generated from the following files:\begin{CompactItemize}
\item 
\hyperlink{libKF_8h}{libKF.h}\item 
libKF.cpp\end{CompactItemize}
