root/doc/latex/classbdm_1_1BM.tex @ 271

\hypertarget{classbdm_1_1BM}{
\section{bdm::BM Class Reference}
\label{classbdm_1_1BM}\index{bdm::BM@{bdm::BM}}
}
{\tt \#include $<$libBM.h$>$}

Inheritance diagram for bdm::BM::\begin{figure}[H]
\begin{center}
\leavevmode
\includegraphics[height=2.50559cm]{classbdm_1_1BM}
\end{center}
\end{figure}


\subsection{Detailed Description}
Bayesian Model of a system, i.e. all uncertainty is modeled by probabilities. 

\subsection*{Public Member Functions}
\begin{Indent}{\bf Constructors}\par
\begin{CompactItemize}
\item 
\hypertarget{classbdm_1_1BM_db12aecc3135e7868c664e39e7133756}{
\textbf{BM} ()}
\label{classbdm_1_1BM_db12aecc3135e7868c664e39e7133756}

\item 
\hypertarget{classbdm_1_1BM_241b3701190ff1f729fe873a2eef0055}{
\textbf{BM} (const \hyperlink{classbdm_1_1BM}{BM} \&B)}
\label{classbdm_1_1BM_241b3701190ff1f729fe873a2eef0055}

\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 
virtual void \hyperlink{classbdm_1_1BM_60b1779a577367c369a932cabd3a6188}{bayes} (const vec \&dt)=0
\begin{CompactList}\small\item\em Incremental Bayes rule. \item\end{CompactList}\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_b38d92f17620813ad872d86e01a26e5e}{
void \textbf{set\_\-rv} (const \hyperlink{classbdm_1_1RV}{RV} \&rv)}
\label{classbdm_1_1BM_b38d92f17620813ad872d86e01a26e5e}

\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}

\item 
\hypertarget{classbdm_1_1BM_bb7b0065d6cb722a66b371a8260121e1}{
virtual const \hyperlink{classbdm_1_1epdf}{epdf} \& \textbf{posterior} () const =0}
\label{classbdm_1_1BM_bb7b0065d6cb722a66b371a8260121e1}

\item 
\hypertarget{classbdm_1_1BM_4ed0f8b880e606316ae800f3a011c3a6}{
virtual const \hyperlink{classbdm_1_1epdf}{epdf} $\ast$ \textbf{\_\-e} () const =0}
\label{classbdm_1_1BM_4ed0f8b880e606316ae800f3a011c3a6}

\end{CompactItemize}
\end{Indent}
\subsection*{Protected Attributes}
\begin{CompactItemize}
\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{Member Function Documentation}
\hypertarget{classbdm_1_1BM_c0f027ff91d8459937c6f60ff8e553ff}{
\index{bdm::BM@{bdm::BM}!\_\-copy\_\-@{\_\-copy\_\-}}
\index{\_\-copy\_\-@{\_\-copy\_\-}!bdm::BM@{bdm::BM}}
\subsubsection[\_\-copy\_\-]{\setlength{\rightskip}{0pt plus 5cm}virtual {\bf BM}$\ast$ bdm::BM::\_\-copy\_\- ()\hspace{0.3cm}{\tt  \mbox{[}inline, virtual\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_60b1779a577367c369a932cabd3a6188}{
\index{bdm::BM@{bdm::BM}!bayes@{bayes}}
\index{bayes@{bayes}!bdm::BM@{bdm::BM}}
\subsubsection[bayes]{\setlength{\rightskip}{0pt plus 5cm}virtual void bdm::BM::bayes (const vec \& {\em dt})\hspace{0.3cm}{\tt  \mbox{[}pure virtual\mbox{]}}}}
\label{classbdm_1_1BM_60b1779a577367c369a932cabd3a6188}


Incremental Bayes rule. 

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


Implemented in \hyperlink{classbdm_1_1ARX_8bdf2974052e8ce74eb0d4f3791c58a3}{bdm::ARX}, \hyperlink{classbdm_1_1Kalman_4a39330c14eff8d13179e868a1d1aa8c}{bdm::Kalman$<$ sq\_\-T $>$}, \hyperlink{classbdm_1_1KalmanCh_b41fe5540548100b08e1684c3be767b6}{bdm::KalmanCh}, \hyperlink{classbdm_1_1EKFfull_f149ae8e9ce14d9931a7bb2850736699}{bdm::EKFfull}, \hyperlink{classbdm_1_1EKF_3fb182ecc29b10ca1163cecbf3bcccfa}{bdm::EKF$<$ sq\_\-T $>$}, \hyperlink{classbdm_1_1EKFCh_4c8609c37290b158f88a31dae4047225}{bdm::EKFCh}, \hyperlink{classbdm_1_1PF_638946eea22d4964bf9350286bb4efd8}{bdm::PF}, \hyperlink{classbdm_1_1MPF_286d040770d08bd7ff416cea617b1b14}{bdm::MPF$<$ BM\_\-T $>$}, \hyperlink{classbdm_1_1MixEF_5bd7da667da183eed1577f11dff0c1f1}{bdm::MixEF}, \hyperlink{classbdm_1_1BMEF_c287f4c0c1ea31b91572ec45351838f1}{bdm::BMEF}, \hyperlink{classbdm_1_1multiBM_1e4bf41b61937fd80f34049742e23f95}{bdm::multiBM}, \hyperlink{classbdm_1_1Kalman_4a39330c14eff8d13179e868a1d1aa8c}{bdm::Kalman$<$ ldmat $>$}, \hyperlink{classbdm_1_1Kalman_4a39330c14eff8d13179e868a1d1aa8c}{bdm::Kalman$<$ chmat $>$}, and \hyperlink{classbdm_1_1Kalman_4a39330c14eff8d13179e868a1d1aa8c}{bdm::Kalman$<$ fsqmat $>$}.

Referenced by bayesB().\hypertarget{classbdm_1_1BM_50257e0c1e5b5c73153ea6e716ad8ae0}{
\index{bdm::BM@{bdm::BM}!logpred@{logpred}}
\index{logpred@{logpred}!bdm::BM@{bdm::BM}}
\subsubsection[logpred]{\setlength{\rightskip}{0pt plus 5cm}virtual double bdm::BM::logpred (const vec \& {\em dt}) const\hspace{0.3cm}{\tt  \mbox{[}inline, virtual\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 logpred\_\-m().

The documentation for this class was generated from the following files:\begin{CompactItemize}
\item 
\hyperlink{libBM_8h}{libBM.h}\item 
libBM.cpp\end{CompactItemize}