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1\hypertarget{classeigamma}{
2\section{eigamma Class Reference}
3\label{classeigamma}\index{eigamma@{eigamma}}
4}
5Inverse-Gamma posterior density. 
6
7
8{\tt \#include $<$libEF.h$>$}
9
10Inheritance diagram for eigamma:\nopagebreak
11\begin{figure}[H]
12\begin{center}
13\leavevmode
14\includegraphics[width=53pt]{classeigamma__inherit__graph}
15\end{center}
16\end{figure}
17Collaboration diagram for eigamma:\nopagebreak
18\begin{figure}[H]
19\begin{center}
20\leavevmode
21\includegraphics[width=70pt]{classeigamma__coll__graph}
22\end{center}
23\end{figure}
24\subsection*{Public Member Functions}
25\begin{CompactItemize}
26\item 
27\hypertarget{classeigamma_ea0edc0a1f32350219f55cf35d83a5f6}{
28\hyperlink{classeigamma_ea0edc0a1f32350219f55cf35d83a5f6}{eigamma} (const \hyperlink{classRV}{RV} \&\hyperlink{classepdf_74da992e3f5d598da8850b646b79b9d9}{rv})}
29\label{classeigamma_ea0edc0a1f32350219f55cf35d83a5f6}
30
31\begin{CompactList}\small\item\em Default constructor. \item\end{CompactList}\item 
32\hypertarget{classeigamma_a86b94a5f9189cae1b6651838dc153aa}{
33void \hyperlink{classeigamma_a86b94a5f9189cae1b6651838dc153aa}{set\_\-parameters} (const vec \&a, const vec \&b)}
34\label{classeigamma_a86b94a5f9189cae1b6651838dc153aa}
35
36\begin{CompactList}\small\item\em Sets parameters. \item\end{CompactList}\item 
37\hypertarget{classeigamma_b70deffdf41b590377fd6743e4d306f1}{
38vec \hyperlink{classeigamma_b70deffdf41b590377fd6743e4d306f1}{sample} () const }
39\label{classeigamma_b70deffdf41b590377fd6743e4d306f1}
40
41\begin{CompactList}\small\item\em Returns a sample, $x$ from density $epdf(rv)$. \item\end{CompactList}\item 
42\hypertarget{classeigamma_960cf366101389f58f11c5f748dd7e80}{
43double \hyperlink{classeigamma_960cf366101389f58f11c5f748dd7e80}{evallog} (const vec \&val) const }
44\label{classeigamma_960cf366101389f58f11c5f748dd7e80}
45
46\begin{CompactList}\small\item\em TODO: is it used anywhere? \item\end{CompactList}\item 
47\hypertarget{classeigamma_efcc280de487d8b81f9b31f286404c72}{
48double \hyperlink{classeigamma_efcc280de487d8b81f9b31f286404c72}{lognc} () const }
49\label{classeigamma_efcc280de487d8b81f9b31f286404c72}
50
51\begin{CompactList}\small\item\em logarithm of the normalizing constant, $\mathcal{I}$ \item\end{CompactList}\item 
52\hypertarget{classeigamma_86389685695f6948d2e52070cd89a9ed}{
53void \hyperlink{classeigamma_86389685695f6948d2e52070cd89a9ed}{\_\-param} (vec $\ast$\&a, vec $\ast$\&b)}
54\label{classeigamma_86389685695f6948d2e52070cd89a9ed}
55
56\begin{CompactList}\small\item\em Returns poiter to alpha and beta. Potentially dangerous: use with care! \item\end{CompactList}\item 
57\hypertarget{classeigamma_0ff10e82b0f0d07c2dd4ff5f23b3c70f}{
58vec \hyperlink{classeigamma_0ff10e82b0f0d07c2dd4ff5f23b3c70f}{mean} () const }
59\label{classeigamma_0ff10e82b0f0d07c2dd4ff5f23b3c70f}
60
61\begin{CompactList}\small\item\em return expected value \item\end{CompactList}\item 
62\hypertarget{classeigamma_a9ad6cb7514ffc46605f28316eda54ff}{
63vec \hyperlink{classeigamma_a9ad6cb7514ffc46605f28316eda54ff}{variance} () const }
64\label{classeigamma_a9ad6cb7514ffc46605f28316eda54ff}
65
66\begin{CompactList}\small\item\em return expected variance (not covariance!) \item\end{CompactList}\item 
67\hypertarget{classeEF_a89bef8996410609004fa019b5b48964}{
68virtual void \hyperlink{classeEF_a89bef8996410609004fa019b5b48964}{dupdate} (mat \&v)}
69\label{classeEF_a89bef8996410609004fa019b5b48964}
70
71\begin{CompactList}\small\item\em TODO decide if it is really needed. \item\end{CompactList}\item 
72\hypertarget{classeEF_41c70565b4d3fb424599817d008f0c71}{
73virtual double \hyperlink{classeEF_41c70565b4d3fb424599817d008f0c71}{evallog\_\-nn} (const vec \&val) const }
74\label{classeEF_41c70565b4d3fb424599817d008f0c71}
75
76\begin{CompactList}\small\item\em Evaluate normalized log-probability. \item\end{CompactList}\item 
77\hypertarget{classeEF_cff03a658aec11b806c3e3d48f37b81f}{
78virtual vec \hyperlink{classeEF_cff03a658aec11b806c3e3d48f37b81f}{evallog} (const mat \&Val) const }
79\label{classeEF_cff03a658aec11b806c3e3d48f37b81f}
80
81\begin{CompactList}\small\item\em Evaluate normalized log-probability for many samples. \item\end{CompactList}\item 
82\hypertarget{classeEF_4f8385dd1cc9740522dc373b1dc3cbf5}{
83virtual void \hyperlink{classeEF_4f8385dd1cc9740522dc373b1dc3cbf5}{pow} (double p)}
84\label{classeEF_4f8385dd1cc9740522dc373b1dc3cbf5}
85
86\begin{CompactList}\small\item\em Power of the density, used e.g. to flatten the density. \item\end{CompactList}\item 
87\hypertarget{classepdf_76608914c3b19e150292d5c56e93e508}{
88virtual mat \hyperlink{classepdf_76608914c3b19e150292d5c56e93e508}{sample\_\-m} (int N) const }
89\label{classepdf_76608914c3b19e150292d5c56e93e508}
90
91\begin{CompactList}\small\item\em Returns N samples from density $epdf(rv)$. \item\end{CompactList}\item 
92\hypertarget{classepdf_2495a04bbacb9b55fe5a3a59b78affca}{
93virtual vec \hyperlink{classepdf_2495a04bbacb9b55fe5a3a59b78affca}{evallog\_\-m} (const mat \&Val) const }
94\label{classepdf_2495a04bbacb9b55fe5a3a59b78affca}
95
96\begin{CompactList}\small\item\em Compute log-probability of multiple values argument {\tt val}. \item\end{CompactList}\item 
97\hypertarget{classepdf_e87dc8260a5c37bc1b03eb66174741a0}{
98virtual \hyperlink{classmpdf}{mpdf} $\ast$ \hyperlink{classepdf_e87dc8260a5c37bc1b03eb66174741a0}{condition} (const \hyperlink{classRV}{RV} \&\hyperlink{classepdf_74da992e3f5d598da8850b646b79b9d9}{rv}) const }
99\label{classepdf_e87dc8260a5c37bc1b03eb66174741a0}
100
101\begin{CompactList}\small\item\em Return conditional density on the given \hyperlink{classRV}{RV}, the remaining rvs will be in conditioning. \item\end{CompactList}\item 
102\hypertarget{classepdf_38de9f59b65ee06028554f3f74b66025}{
103virtual \hyperlink{classepdf}{epdf} $\ast$ \hyperlink{classepdf_38de9f59b65ee06028554f3f74b66025}{marginal} (const \hyperlink{classRV}{RV} \&\hyperlink{classepdf_74da992e3f5d598da8850b646b79b9d9}{rv}) const }
104\label{classepdf_38de9f59b65ee06028554f3f74b66025}
105
106\begin{CompactList}\small\item\em Return marginal density on the given \hyperlink{classRV}{RV}, the remainig rvs are intergrated out. \item\end{CompactList}\item 
107\hypertarget{classepdf_ca0d32aabb4cbba347e0c37fe8607562}{
108const \hyperlink{classRV}{RV} \& \hyperlink{classepdf_ca0d32aabb4cbba347e0c37fe8607562}{\_\-rv} () const }
109\label{classepdf_ca0d32aabb4cbba347e0c37fe8607562}
110
111\begin{CompactList}\small\item\em access function, possibly dangerous! \item\end{CompactList}\item 
112\hypertarget{classepdf_7fb94ce90d1ac7077d29f7d6a6c3e0a5}{
113void \hyperlink{classepdf_7fb94ce90d1ac7077d29f7d6a6c3e0a5}{\_\-renewrv} (const \hyperlink{classRV}{RV} \&in\_\-rv)}
114\label{classepdf_7fb94ce90d1ac7077d29f7d6a6c3e0a5}
115
116\begin{CompactList}\small\item\em modifier function - useful when copying epdfs \item\end{CompactList}\end{CompactItemize}
117\subsection*{Protected Attributes}
118\begin{CompactItemize}
119\item 
120\hypertarget{classeigamma_ea00e33f405ebd918e06cede968a735b}{
121vec $\ast$ \hyperlink{classeigamma_ea00e33f405ebd918e06cede968a735b}{alpha}}
122\label{classeigamma_ea00e33f405ebd918e06cede968a735b}
123
124\begin{CompactList}\small\item\em Vector $\alpha$. \item\end{CompactList}\item 
125\hypertarget{classeigamma_ee446ec667a4df391e0db41decb2d558}{
126vec $\ast$ \hyperlink{classeigamma_ee446ec667a4df391e0db41decb2d558}{beta}}
127\label{classeigamma_ee446ec667a4df391e0db41decb2d558}
128
129\begin{CompactList}\small\item\em Vector $\beta$ (in fact it is 1/beta as used in definition of iG). \item\end{CompactList}\item 
130\hypertarget{classeigamma_906f2a3a8fbf08b2af49776f2f1be5d4}{
131\hyperlink{classegamma}{egamma} \hyperlink{classeigamma_906f2a3a8fbf08b2af49776f2f1be5d4}{eg}}
132\label{classeigamma_906f2a3a8fbf08b2af49776f2f1be5d4}
133
134\begin{CompactList}\small\item\em internal \hyperlink{classegamma}{egamma} \item\end{CompactList}\item 
135\hypertarget{classepdf_74da992e3f5d598da8850b646b79b9d9}{
136\hyperlink{classRV}{RV} \hyperlink{classepdf_74da992e3f5d598da8850b646b79b9d9}{rv}}
137\label{classepdf_74da992e3f5d598da8850b646b79b9d9}
138
139\begin{CompactList}\small\item\em Identified of the random variable. \item\end{CompactList}\end{CompactItemize}
140
141
142\subsection{Detailed Description}
143Inverse-Gamma posterior density.
144
145Multivariate inverse-Gamma density as product of independent univariate densities. \[ f(x|\alpha,\beta) = \prod f(x_i|\alpha_i,\beta_i) \]
146
147Inverse Gamma can be converted to Gamma using $\backslash$\mbox{[} x iG(a,b) =$>$ 1/x G(a,1/b) $\backslash$\mbox{]} This relation is used in sampling.
148
149The documentation for this class was generated from the following file:\begin{CompactItemize}
150\item 
151work/git/mixpp/bdm/stat/\hyperlink{libEF_8h}{libEF.h}\end{CompactItemize}
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