[19] | 1 | \section{work/mixpp/bdm/stat/libBM.h File Reference} |
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| 2 | \label{libBM_8h}\index{work/mixpp/bdm/stat/libBM.h@{work/mixpp/bdm/stat/libBM.h}} |
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[3] | 3 | Bayesian Models (bm) that use Bayes rule to learn from observations. |
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| 4 | |
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| 5 | {\tt \#include $<$itpp/itbase.h$>$}\par |
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[19] | 6 | |
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| 7 | |
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| 8 | Include dependency graph for libBM.h:\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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[91] | 12 | \includegraphics[width=93pt]{libBM_8h__incl} |
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[19] | 13 | \end{center} |
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| 14 | \end{figure} |
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| 15 | |
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| 16 | |
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| 17 | This graph shows which files directly or indirectly include this file:\nopagebreak |
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| 18 | \begin{figure}[H] |
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| 19 | \begin{center} |
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| 20 | \leavevmode |
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[106] | 21 | \includegraphics[width=420pt]{libBM_8h__dep__incl} |
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[19] | 22 | \end{center} |
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| 23 | \end{figure} |
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[3] | 24 | \subsection*{Classes} |
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| 25 | \begin{CompactItemize} |
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| 26 | \item |
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[4] | 27 | class {\bf RV} |
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| 28 | \begin{CompactList}\small\item\em Class representing variables, most often random variables. \item\end{CompactList}\item |
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[3] | 29 | class {\bf fnc} |
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[91] | 30 | \begin{CompactList}\small\item\em Class representing function $f(x)$ of variable $x$ represented by {\tt rv}. \item\end{CompactList}\item |
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[4] | 31 | class {\bf epdf} |
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| 32 | \begin{CompactList}\small\item\em Probability density function with numerical statistics, e.g. posterior density. \item\end{CompactList}\item |
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| 33 | class {\bf mpdf} |
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[19] | 34 | \begin{CompactList}\small\item\em Conditional probability density, e.g. modeling some dependencies. \item\end{CompactList}\item |
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[140] | 35 | class {\bf mepdf} |
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| 36 | \begin{CompactList}\small\item\em Unconditional \doxyref{mpdf}{p.}{classmpdf}, allows using \doxyref{epdf}{p.}{classepdf} in the role of \doxyref{mpdf}{p.}{classmpdf}. \item\end{CompactList}\item |
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[19] | 37 | class {\bf DS} |
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[32] | 38 | \begin{CompactList}\small\item\em Abstract class for discrete-time sources of data. \item\end{CompactList}\item |
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| 39 | class {\bf BM} |
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| 40 | \begin{CompactList}\small\item\em Bayesian Model of the world, i.e. all uncertainty is modeled by probabilities. \item\end{CompactList}\item |
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| 41 | class {\bf BMcond} |
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| 42 | \begin{CompactList}\small\item\em Conditional Bayesian Filter. \item\end{CompactList}\end{CompactItemize} |
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[3] | 43 | |
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| 44 | |
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| 45 | \subsection{Detailed Description} |
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| 46 | Bayesian Models (bm) that use Bayes rule to learn from observations. |
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| 47 | |
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| 48 | \begin{Desc} |
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| 49 | \item[Author:]Vaclav Smidl.\end{Desc} |
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| 50 | ----------------------------------- BDM++ - C++ library for Bayesian Decision Making under Uncertainty |
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| 51 | |
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| 52 | Using IT++ for numerical operations ----------------------------------- |
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