| 1 | \section{BM Class Reference} |
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| 2 | \label{classBM}\index{BM@{BM}} |
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| 3 | Bayesian Model of the world, i.e. all uncertainty is modeled by probabilities. |
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| 4 | |
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| 5 | |
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| 6 | {\tt \#include $<$libBM.h$>$} |
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| 7 | |
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| 8 | Inheritance diagram for BM:\nopagebreak |
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| 9 | \begin{figure}[H] |
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| 10 | \begin{center} |
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| 11 | \leavevmode |
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| 12 | \includegraphics[width=161pt]{classBM__inherit__graph} |
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| 13 | \end{center} |
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| 14 | \end{figure} |
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| 15 | \subsection*{Public Member Functions} |
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| 16 | \begin{CompactItemize} |
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| 17 | \item |
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| 18 | {\bf BM} ()\label{classBM_ef32a12f4f89e4000bf5390ceda762ae} |
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| 19 | |
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| 20 | \begin{CompactList}\small\item\em Default constructor. \item\end{CompactList}\item |
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| 21 | virtual void {\bf bayes} (const vec \&dt, bool evall=true)=0 |
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| 22 | \begin{CompactList}\small\item\em Incremental Bayes rule. \item\end{CompactList}\item |
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| 23 | void {\bf bayes} (mat Dt)\label{classBM_87b07867fd4c133aa89a18543f68d9f9} |
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| 24 | |
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| 25 | \begin{CompactList}\small\item\em Batch Bayes rule (columns of Dt are observations). \item\end{CompactList}\end{CompactItemize} |
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| 26 | \subsection*{Public Attributes} |
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| 27 | \begin{CompactItemize} |
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| 28 | \item |
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| 29 | double {\bf ll}\label{classBM_5623fef6572a08c2b53b8c87b82dc979} |
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| 30 | |
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| 31 | \begin{CompactList}\small\item\em Logarithm of marginalized data likelihood. \item\end{CompactList}\end{CompactItemize} |
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| 32 | |
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| 33 | |
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| 34 | \subsection{Detailed Description} |
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| 35 | Bayesian Model of the world, i.e. all uncertainty is modeled by probabilities. |
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| 36 | |
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| 37 | \subsection{Member Function Documentation} |
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| 38 | \index{BM@{BM}!bayes@{bayes}} |
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| 39 | \index{bayes@{bayes}!BM@{BM}} |
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| 40 | \subsubsection{\setlength{\rightskip}{0pt plus 5cm}virtual void BM::bayes (const vec \& {\em dt}, bool {\em evall} = {\tt true})\hspace{0.3cm}{\tt [pure virtual]}}\label{classBM_c52edf4ad6e1dff9bf64b9e1e0cfb1f0} |
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| 41 | |
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| 42 | |
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| 43 | Incremental Bayes rule. |
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| 44 | |
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| 45 | \begin{Desc} |
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| 46 | \item[Parameters:] |
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| 47 | \begin{description} |
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| 48 | \item[{\em dt}]vector of input data \item[{\em evall}]If true, the filter will compute likelihood of the data record and store it in {\tt ll} \end{description} |
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| 49 | \end{Desc} |
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| 50 | |
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| 51 | |
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| 52 | Implemented in {\bf KalmanFull} \doxyref{}{p.}{classKalmanFull_048b13739b94c331cda08249b278552b}, {\bf Kalman$<$ sq\_\-T $>$} \doxyref{}{p.}{classKalman_e945d9205ca14acbd83ba80ea6f72b8e}, {\bf EKF$<$ sq\_\-T $>$} \doxyref{}{p.}{classEKF_fb0a08463f14e5584344ea2df99fe747}, {\bf PF} \doxyref{}{p.}{classPF_eb06bd7d4325f22f54233967295793b9}, {\bf TrivialPF} \doxyref{}{p.}{classTrivialPF_77a92bf054d763f806d27fc37a058389}, and {\bf Kalman$<$ fsqmat $>$} \doxyref{}{p.}{classKalman_e945d9205ca14acbd83ba80ea6f72b8e}. |
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| 53 | |
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| 54 | The documentation for this class was generated from the following file:\begin{CompactItemize} |
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| 55 | \item |
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| 56 | work/mixpp/bdm/stat/{\bf libBM.h}\end{CompactItemize} |
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