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test KF : estimation of R in KF is not possible! Likelihood of y_t is growing when R -> 0

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1\section{work/mixpp/bdm/stat/libEF.h File Reference}
2\label{libEF_8h}\index{work/mixpp/bdm/stat/libEF.h@{work/mixpp/bdm/stat/libEF.h}}
3Probability distributions for Exponential Family models.
4
5{\tt \#include $<$itpp/itbase.h$>$}\par
6{\tt \#include \char`\"{}../math/libDC.h\char`\"{}}\par
7{\tt \#include \char`\"{}libBM.h\char`\"{}}\par
8{\tt \#include \char`\"{}../itpp\_\-ext.h\char`\"{}}\par
9
10
11Include dependency graph for libEF.h:\nopagebreak
12\begin{figure}[H]
13\begin{center}
14\leavevmode
15\includegraphics[width=157pt]{libEF_8h__incl}
16\end{center}
17\end{figure}
18
19
20This graph shows which files directly or indirectly include this file:\nopagebreak
21\begin{figure}[H]
22\begin{center}
23\leavevmode
24\includegraphics[width=191pt]{libEF_8h__dep__incl}
25\end{center}
26\end{figure}
27\subsection*{Classes}
28\begin{CompactItemize}
29\item 
30class {\bf eEF}
31\begin{CompactList}\small\item\em General conjugate exponential family posterior density. \item\end{CompactList}\item 
32class \textbf{mEF}
33\item 
34class {\bf enorm$<$ sq\_\-T $>$}
35\begin{CompactList}\small\item\em Gaussian density with positive definite (decomposed) covariance matrix. \item\end{CompactList}\item 
36class {\bf egamma}
37\begin{CompactList}\small\item\em Gamma posterior density. \item\end{CompactList}\item 
38class {\bf emix}
39\begin{CompactList}\small\item\em Weighted mixture of epdfs with external owned components. \item\end{CompactList}\item 
40class {\bf euni}
41\begin{CompactList}\small\item\em Uniform distributed density on a rectangular support. \item\end{CompactList}\item 
42class {\bf mlnorm$<$ sq\_\-T $>$}
43\begin{CompactList}\small\item\em Normal distributed linear function with linear function of mean value;. \item\end{CompactList}\item 
44class {\bf mgamma}
45\begin{CompactList}\small\item\em Gamma random walk. \item\end{CompactList}\item 
46class {\bf eEmp}
47\begin{CompactList}\small\item\em Weighted empirical density. \item\end{CompactList}\end{CompactItemize}
48\subsection*{Enumerations}
49\begin{CompactItemize}
50\item 
51enum {\bf RESAMPLING\_\-METHOD} \{ \textbf{MULTINOMIAL} =  0,
52\textbf{STRATIFIED} =  1,
53\textbf{SYSTEMATIC} =  3
54 \}
55\begin{CompactList}\small\item\em Switch between various resampling methods. \item\end{CompactList}\end{CompactItemize}
56\subsection*{Variables}
57\begin{CompactItemize}
58\item 
59Uniform\_\-RNG {\bf UniRNG}\label{libEF_8h_2ae7dcdfebede774dd1b1f16cad10dd9}
60
61\begin{CompactList}\small\item\em Global Uniform\_\-RNG. \item\end{CompactList}\item 
62Normal\_\-RNG \textbf{NorRNG}\label{libEF_8h_395c5925c8792aef3be4c360e91526c0}
63
64\item 
65{\bf Gamma\_\-RNG} \textbf{GamRNG}\label{libEF_8h_884a8348c92a49725b78e2b6ab0bb802}
66
67\end{CompactItemize}
68
69
70\subsection{Detailed Description}
71Probability distributions for Exponential Family models.
72
73\begin{Desc}
74\item[Author:]Vaclav Smidl.\end{Desc}
75----------------------------------- BDM++ - C++ library for Bayesian Decision Making under Uncertainty
76
77Using IT++ for numerical operations -----------------------------------
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