root/doc/latex/classegamma.tex @ 180

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Modifications of BDM to reflect changes in basics

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1\hypertarget{classegamma}{
2\section{egamma Class Reference}
3\label{classegamma}\index{egamma@{egamma}}
4}
5Gamma posterior density. 
6
7
8{\tt \#include $<$libEF.h$>$}
9
10Inheritance diagram for egamma:\nopagebreak
11\begin{figure}[H]
12\begin{center}
13\leavevmode
14\includegraphics[width=49pt]{classegamma__inherit__graph}
15\end{center}
16\end{figure}
17Collaboration diagram for egamma:\nopagebreak
18\begin{figure}[H]
19\begin{center}
20\leavevmode
21\includegraphics[width=49pt]{classegamma__coll__graph}
22\end{center}
23\end{figure}
24\subsection*{Public Member Functions}
25\begin{CompactItemize}
26\item 
27\hypertarget{classegamma_4b1d34f3b244ea51a58ec10c468788c1}{
28\hyperlink{classegamma_4b1d34f3b244ea51a58ec10c468788c1}{egamma} (const \hyperlink{classRV}{RV} \&\hyperlink{classepdf_74da992e3f5d598da8850b646b79b9d9}{rv})}
29\label{classegamma_4b1d34f3b244ea51a58ec10c468788c1}
30
31\begin{CompactList}\small\item\em Default constructor. \item\end{CompactList}\item 
32\hypertarget{classegamma_8e348b89be82b70471fe8c5630f61339}{
33void \hyperlink{classegamma_8e348b89be82b70471fe8c5630f61339}{set\_\-parameters} (const vec \&a, const vec \&b)}
34\label{classegamma_8e348b89be82b70471fe8c5630f61339}
35
36\begin{CompactList}\small\item\em Sets parameters. \item\end{CompactList}\item 
37\hypertarget{classegamma_8e10c0021b5dfdd9cb62c6959b5ef425}{
38vec \hyperlink{classegamma_8e10c0021b5dfdd9cb62c6959b5ef425}{sample} () const }
39\label{classegamma_8e10c0021b5dfdd9cb62c6959b5ef425}
40
41\begin{CompactList}\small\item\em Returns a sample, $x$ from density $epdf(rv)$. \item\end{CompactList}\item 
42\hypertarget{classegamma_de84faac8f9799dfe2777ddbedf997ef}{
43double \hyperlink{classegamma_de84faac8f9799dfe2777ddbedf997ef}{evalpdflog} (const vec \&val) const }
44\label{classegamma_de84faac8f9799dfe2777ddbedf997ef}
45
46\begin{CompactList}\small\item\em TODO: is it used anywhere? \item\end{CompactList}\item 
47\hypertarget{classegamma_d6dbbdb72360f9e54d64501f80318bb6}{
48double \hyperlink{classegamma_d6dbbdb72360f9e54d64501f80318bb6}{lognc} () const }
49\label{classegamma_d6dbbdb72360f9e54d64501f80318bb6}
50
51\begin{CompactList}\small\item\em logarithm of the normalizing constant, $\mathcal{I}$ \item\end{CompactList}\item 
52\hypertarget{classegamma_44445c56e60b91b377f207f8d5089790}{
53void \hyperlink{classegamma_44445c56e60b91b377f207f8d5089790}{\_\-param} (vec $\ast$\&a, vec $\ast$\&b)}
54\label{classegamma_44445c56e60b91b377f207f8d5089790}
55
56\begin{CompactList}\small\item\em Returns poiter to alpha and beta. Potentially dengerous: use with care! \item\end{CompactList}\item 
57\hypertarget{classegamma_6ab5ba56f7cdb2e5921c3e77524fa50a}{
58vec \hyperlink{classegamma_6ab5ba56f7cdb2e5921c3e77524fa50a}{mean} () const }
59\label{classegamma_6ab5ba56f7cdb2e5921c3e77524fa50a}
60
61\begin{CompactList}\small\item\em return expected value \item\end{CompactList}\item 
62\hypertarget{classeEF_a89bef8996410609004fa019b5b48964}{
63virtual void \hyperlink{classeEF_a89bef8996410609004fa019b5b48964}{dupdate} (mat \&v)}
64\label{classeEF_a89bef8996410609004fa019b5b48964}
65
66\begin{CompactList}\small\item\em TODO decide if it is really needed. \item\end{CompactList}\item 
67\hypertarget{classeEF_48cdd33d0e20d1a1aa45683c956bc61c}{
68virtual double \hyperlink{classeEF_48cdd33d0e20d1a1aa45683c956bc61c}{evalpdflog\_\-nn} (const vec \&val) const }
69\label{classeEF_48cdd33d0e20d1a1aa45683c956bc61c}
70
71\begin{CompactList}\small\item\em Evaluate normalized log-probability. \item\end{CompactList}\item 
72\hypertarget{classeEF_c71faf4b2d153efda14bf1f87dca1507}{
73virtual vec \hyperlink{classeEF_c71faf4b2d153efda14bf1f87dca1507}{evalpdflog} (const mat \&Val) const }
74\label{classeEF_c71faf4b2d153efda14bf1f87dca1507}
75
76\begin{CompactList}\small\item\em Evaluate normalized log-probability for many samples. \item\end{CompactList}\item 
77\hypertarget{classeEF_4f8385dd1cc9740522dc373b1dc3cbf5}{
78virtual void \hyperlink{classeEF_4f8385dd1cc9740522dc373b1dc3cbf5}{pow} (double p)}
79\label{classeEF_4f8385dd1cc9740522dc373b1dc3cbf5}
80
81\begin{CompactList}\small\item\em Power of the density, used e.g. to flatten the density. \item\end{CompactList}\item 
82\hypertarget{classepdf_54d7dd53a641b618771cd9bee135181f}{
83virtual mat \hyperlink{classepdf_54d7dd53a641b618771cd9bee135181f}{sampleN} (int N) const }
84\label{classepdf_54d7dd53a641b618771cd9bee135181f}
85
86\begin{CompactList}\small\item\em Returns N samples from density $epdf(rv)$. \item\end{CompactList}\item 
87\hypertarget{classepdf_3ea597362e11a0040fe7c990269d072c}{
88virtual double \hyperlink{classepdf_3ea597362e11a0040fe7c990269d072c}{eval} (const vec \&val) const }
89\label{classepdf_3ea597362e11a0040fe7c990269d072c}
90
91\begin{CompactList}\small\item\em Compute probability of argument {\tt val}. \item\end{CompactList}\item 
92\hypertarget{classepdf_cebbdd7a85e6328f7358fc0ba8eee06c}{
93virtual vec \hyperlink{classepdf_cebbdd7a85e6328f7358fc0ba8eee06c}{evalpdflog\_\-m} (const mat \&Val) const }
94\label{classepdf_cebbdd7a85e6328f7358fc0ba8eee06c}
95
96\begin{CompactList}\small\item\em Compute log-probability of multiple values argument {\tt val}. \item\end{CompactList}\item 
97\hypertarget{classepdf_ca0d32aabb4cbba347e0c37fe8607562}{
98const \hyperlink{classRV}{RV} \& \hyperlink{classepdf_ca0d32aabb4cbba347e0c37fe8607562}{\_\-rv} () const }
99\label{classepdf_ca0d32aabb4cbba347e0c37fe8607562}
100
101\begin{CompactList}\small\item\em access function, possibly dangerous! \item\end{CompactList}\item 
102\hypertarget{classepdf_7fb94ce90d1ac7077d29f7d6a6c3e0a5}{
103void \hyperlink{classepdf_7fb94ce90d1ac7077d29f7d6a6c3e0a5}{\_\-renewrv} (const \hyperlink{classRV}{RV} \&in\_\-rv)}
104\label{classepdf_7fb94ce90d1ac7077d29f7d6a6c3e0a5}
105
106\begin{CompactList}\small\item\em modifier function - useful when copying epdfs \item\end{CompactList}\end{CompactItemize}
107\subsection*{Protected Attributes}
108\begin{CompactItemize}
109\item 
110\hypertarget{classegamma_376cebd8932546c440f21b182910b01b}{
111vec \hyperlink{classegamma_376cebd8932546c440f21b182910b01b}{alpha}}
112\label{classegamma_376cebd8932546c440f21b182910b01b}
113
114\begin{CompactList}\small\item\em Vector $\alpha$. \item\end{CompactList}\item 
115\hypertarget{classegamma_cfc5f136467488a421ab22f886323790}{
116vec \hyperlink{classegamma_cfc5f136467488a421ab22f886323790}{beta}}
117\label{classegamma_cfc5f136467488a421ab22f886323790}
118
119\begin{CompactList}\small\item\em Vector $\beta$. \item\end{CompactList}\item 
120\hypertarget{classepdf_74da992e3f5d598da8850b646b79b9d9}{
121\hyperlink{classRV}{RV} \hyperlink{classepdf_74da992e3f5d598da8850b646b79b9d9}{rv}}
122\label{classepdf_74da992e3f5d598da8850b646b79b9d9}
123
124\begin{CompactList}\small\item\em Identified of the random variable. \item\end{CompactList}\end{CompactItemize}
125
126
127\subsection{Detailed Description}
128Gamma posterior density.
129
130Multivariate Gamma density as product of independent univariate densities. \[ f(x|\alpha,\beta) = \prod f(x_i|\alpha_i,\beta_i) \] 
131
132The documentation for this class was generated from the following files:\begin{CompactItemize}
133\item 
134work/git/mixpp/bdm/stat/\hyperlink{libEF_8h}{libEF.h}\item 
135work/git/mixpp/bdm/stat/libEF.cpp\end{CompactItemize}
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