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[3] | 1 | \section{work/mixpp/libBM.h File Reference} |
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| 2 | \label{libBM_8h}\index{work/mixpp/libBM.h@{work/mixpp/libBM.h}} |
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| 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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| 6 | \subsection*{Classes} |
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| 7 | \begin{CompactItemize} |
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| 8 | \item |
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[4] | 9 | class {\bf RV} |
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| 10 | \begin{CompactList}\small\item\em Class representing variables, most often random variables. \item\end{CompactList}\item |
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[3] | 11 | class {\bf fnc} |
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| 12 | \begin{CompactList}\small\item\em Class representing function of variables. \item\end{CompactList}\item |
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[4] | 13 | class {\bf BM} |
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| 14 | \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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| 15 | class {\bf epdf} |
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| 16 | \begin{CompactList}\small\item\em Probability density function with numerical statistics, e.g. posterior density. \item\end{CompactList}\item |
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| 17 | class {\bf mpdf} |
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| 18 | \begin{CompactList}\small\item\em Conditional probability density, e.g. modeling some dependencies. \item\end{CompactList}\end{CompactItemize} |
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[3] | 19 | |
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| 20 | |
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| 21 | \subsection{Detailed Description} |
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| 22 | Bayesian Models (bm) that use Bayes rule to learn from observations. |
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| 23 | |
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| 24 | \begin{Desc} |
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| 25 | \item[Author:]Vaclav Smidl.\end{Desc} |
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| 26 | ----------------------------------- BDM++ - C++ library for Bayesian Decision Making under Uncertainty |
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| 27 | |
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| 28 | Using IT++ for numerical operations ----------------------------------- |
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