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1\hypertarget{classegiw}{
2\section{egiw Class Reference}
3\label{classegiw}\index{egiw@{egiw}}
4}
5Gauss-inverse-Wishart density stored in LD form. 
6
7
8{\tt \#include $<$libEF.h$>$}
9
10Inheritance diagram for egiw:\nopagebreak
11\begin{figure}[H]
12\begin{center}
13\leavevmode
14\includegraphics[width=40pt]{classegiw__inherit__graph}
15\end{center}
16\end{figure}
17Collaboration diagram for egiw:\nopagebreak
18\begin{figure}[H]
19\begin{center}
20\leavevmode
21\includegraphics[width=72pt]{classegiw__coll__graph}
22\end{center}
23\end{figure}
24\subsection*{Public Member Functions}
25\begin{CompactItemize}
26\item 
27\hypertarget{classegiw_056c094f01ca1cc308d72162f47617c9}{
28\hyperlink{classegiw_056c094f01ca1cc308d72162f47617c9}{egiw} (\hyperlink{classRV}{RV} \hyperlink{classepdf_74da992e3f5d598da8850b646b79b9d9}{rv}, mat V0, double nu0=-1.0)}
29\label{classegiw_056c094f01ca1cc308d72162f47617c9}
30
31\begin{CompactList}\small\item\em Default constructor, if nu0$<$0 a minimal nu0 will be computed. \item\end{CompactList}\item 
32\hypertarget{classegiw_18c1bf6125652a6dcbca68dd02dddd8d}{
33\hyperlink{classegiw_18c1bf6125652a6dcbca68dd02dddd8d}{egiw} (\hyperlink{classRV}{RV} \hyperlink{classepdf_74da992e3f5d598da8850b646b79b9d9}{rv}, \hyperlink{classldmat}{ldmat} V0, double nu0=-1.0)}
34\label{classegiw_18c1bf6125652a6dcbca68dd02dddd8d}
35
36\begin{CompactList}\small\item\em Full constructor for V in \hyperlink{classldmat}{ldmat} form. \item\end{CompactList}\item 
37\hypertarget{classegiw_3d2c1f2ba0f9966781f1e0ae695e8a6f}{
38vec \hyperlink{classegiw_3d2c1f2ba0f9966781f1e0ae695e8a6f}{sample} () const }
39\label{classegiw_3d2c1f2ba0f9966781f1e0ae695e8a6f}
40
41\begin{CompactList}\small\item\em Returns a sample, $x$ from density $epdf(rv)$. \item\end{CompactList}\item 
42\hypertarget{classegiw_6deb0ff2859f41ef7cbdf6a842cabb29}{
43vec \hyperlink{classegiw_6deb0ff2859f41ef7cbdf6a842cabb29}{mean} () const }
44\label{classegiw_6deb0ff2859f41ef7cbdf6a842cabb29}
45
46\begin{CompactList}\small\item\em return expected value \item\end{CompactList}\item 
47\hypertarget{classegiw_9594f396acc5ad186d1c5b03b0745502}{
48void \textbf{mean\_\-mat} (mat \&M, mat \&R) const }
49\label{classegiw_9594f396acc5ad186d1c5b03b0745502}
50
51\item 
52\hypertarget{classegiw_2d94daac10d66bb743e4ddc8c1ba7268}{
53double \hyperlink{classegiw_2d94daac10d66bb743e4ddc8c1ba7268}{evallog\_\-nn} (const vec \&val) const }
54\label{classegiw_2d94daac10d66bb743e4ddc8c1ba7268}
55
56\begin{CompactList}\small\item\em In this instance, val= \mbox{[}theta, r\mbox{]}. For multivariate instances, it is stored columnwise val = \mbox{[}theta\_\-1 theta\_\-2 ... r\_\-1 r\_\-2 \mbox{]}. \item\end{CompactList}\item 
57\hypertarget{classegiw_70eb1a0b88459b227f919b425b0d3359}{
58double \hyperlink{classegiw_70eb1a0b88459b227f919b425b0d3359}{lognc} () const }
59\label{classegiw_70eb1a0b88459b227f919b425b0d3359}
60
61\begin{CompactList}\small\item\em logarithm of the normalizing constant, $\mathcal{I}$ \item\end{CompactList}\item 
62\hypertarget{classegiw_533e792e1175bfa06d5d595dc5d080d5}{
63\hyperlink{classldmat}{ldmat} \& \hyperlink{classegiw_533e792e1175bfa06d5d595dc5d080d5}{\_\-V} ()}
64\label{classegiw_533e792e1175bfa06d5d595dc5d080d5}
65
66\begin{CompactList}\small\item\em returns a pointer to the internal statistics. Use with Care! \item\end{CompactList}\item 
67\hypertarget{classegiw_a46c8a206edf80b357a138d7491780c1}{
68const \hyperlink{classldmat}{ldmat} \& \hyperlink{classegiw_a46c8a206edf80b357a138d7491780c1}{\_\-V} () const }
69\label{classegiw_a46c8a206edf80b357a138d7491780c1}
70
71\begin{CompactList}\small\item\em returns a pointer to the internal statistics. Use with Care! \item\end{CompactList}\item 
72\hypertarget{classegiw_08029c481ff95d24f093df0573879afe}{
73double \& \hyperlink{classegiw_08029c481ff95d24f093df0573879afe}{\_\-nu} ()}
74\label{classegiw_08029c481ff95d24f093df0573879afe}
75
76\begin{CompactList}\small\item\em returns a pointer to the internal statistics. Use with Care! \item\end{CompactList}\item 
77\hypertarget{classegiw_5337922a83bc63e9e826e8a8613ebfe8}{
78const double \& \textbf{\_\-nu} () const }
79\label{classegiw_5337922a83bc63e9e826e8a8613ebfe8}
80
81\item 
82\hypertarget{classegiw_036306322a90a9977834baac07460816}{
83void \hyperlink{classegiw_036306322a90a9977834baac07460816}{pow} (double p)}
84\label{classegiw_036306322a90a9977834baac07460816}
85
86\begin{CompactList}\small\item\em Power of the density, used e.g. to flatten the density. \item\end{CompactList}\item 
87\hypertarget{classeEF_a89bef8996410609004fa019b5b48964}{
88virtual void \hyperlink{classeEF_a89bef8996410609004fa019b5b48964}{dupdate} (mat \&v)}
89\label{classeEF_a89bef8996410609004fa019b5b48964}
90
91\begin{CompactList}\small\item\em TODO decide if it is really needed. \item\end{CompactList}\item 
92\hypertarget{classeEF_357512dd565e199904d367294b7dd862}{
93virtual double \hyperlink{classeEF_357512dd565e199904d367294b7dd862}{evallog} (const vec \&val) const }
94\label{classeEF_357512dd565e199904d367294b7dd862}
95
96\begin{CompactList}\small\item\em Evaluate normalized log-probability. \item\end{CompactList}\item 
97\hypertarget{classeEF_cff03a658aec11b806c3e3d48f37b81f}{
98virtual vec \hyperlink{classeEF_cff03a658aec11b806c3e3d48f37b81f}{evallog} (const mat \&Val) const }
99\label{classeEF_cff03a658aec11b806c3e3d48f37b81f}
100
101\begin{CompactList}\small\item\em Evaluate normalized log-probability for many samples. \item\end{CompactList}\item 
102\hypertarget{classepdf_76608914c3b19e150292d5c56e93e508}{
103virtual mat \hyperlink{classepdf_76608914c3b19e150292d5c56e93e508}{sample\_\-m} (int N) const }
104\label{classepdf_76608914c3b19e150292d5c56e93e508}
105
106\begin{CompactList}\small\item\em Returns N samples from density $epdf(rv)$. \item\end{CompactList}\item 
107\hypertarget{classepdf_2495a04bbacb9b55fe5a3a59b78affca}{
108virtual vec \hyperlink{classepdf_2495a04bbacb9b55fe5a3a59b78affca}{evallog\_\-m} (const mat \&Val) const }
109\label{classepdf_2495a04bbacb9b55fe5a3a59b78affca}
110
111\begin{CompactList}\small\item\em Compute log-probability of multiple values argument {\tt val}. \item\end{CompactList}\item 
112\hypertarget{classepdf_e87dc8260a5c37bc1b03eb66174741a0}{
113virtual \hyperlink{classmpdf}{mpdf} $\ast$ \hyperlink{classepdf_e87dc8260a5c37bc1b03eb66174741a0}{condition} (const \hyperlink{classRV}{RV} \&\hyperlink{classepdf_74da992e3f5d598da8850b646b79b9d9}{rv}) const }
114\label{classepdf_e87dc8260a5c37bc1b03eb66174741a0}
115
116\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 
117\hypertarget{classepdf_38de9f59b65ee06028554f3f74b66025}{
118virtual \hyperlink{classepdf}{epdf} $\ast$ \hyperlink{classepdf_38de9f59b65ee06028554f3f74b66025}{marginal} (const \hyperlink{classRV}{RV} \&\hyperlink{classepdf_74da992e3f5d598da8850b646b79b9d9}{rv}) const }
119\label{classepdf_38de9f59b65ee06028554f3f74b66025}
120
121\begin{CompactList}\small\item\em Return marginal density on the given \hyperlink{classRV}{RV}, the remainig rvs are intergrated out. \item\end{CompactList}\item 
122\hypertarget{classepdf_ca0d32aabb4cbba347e0c37fe8607562}{
123const \hyperlink{classRV}{RV} \& \hyperlink{classepdf_ca0d32aabb4cbba347e0c37fe8607562}{\_\-rv} () const }
124\label{classepdf_ca0d32aabb4cbba347e0c37fe8607562}
125
126\begin{CompactList}\small\item\em access function, possibly dangerous! \item\end{CompactList}\item 
127\hypertarget{classepdf_7fb94ce90d1ac7077d29f7d6a6c3e0a5}{
128void \hyperlink{classepdf_7fb94ce90d1ac7077d29f7d6a6c3e0a5}{\_\-renewrv} (const \hyperlink{classRV}{RV} \&in\_\-rv)}
129\label{classepdf_7fb94ce90d1ac7077d29f7d6a6c3e0a5}
130
131\begin{CompactList}\small\item\em modifier function - useful when copying epdfs \item\end{CompactList}\end{CompactItemize}
132\subsection*{Protected Attributes}
133\begin{CompactItemize}
134\item 
135\hypertarget{classegiw_f343d03ede89db820edf44a6297fa442}{
136\hyperlink{classldmat}{ldmat} \hyperlink{classegiw_f343d03ede89db820edf44a6297fa442}{V}}
137\label{classegiw_f343d03ede89db820edf44a6297fa442}
138
139\begin{CompactList}\small\item\em Extended information matrix of sufficient statistics. \item\end{CompactList}\item 
140\hypertarget{classegiw_4a2f130b91afe84f6d62fed289d5d453}{
141double \hyperlink{classegiw_4a2f130b91afe84f6d62fed289d5d453}{nu}}
142\label{classegiw_4a2f130b91afe84f6d62fed289d5d453}
143
144\begin{CompactList}\small\item\em Number of data records (degrees of freedom) of sufficient statistics. \item\end{CompactList}\item 
145\hypertarget{classegiw_3d5c719f15a5527a6c62c2a53160148e}{
146int \hyperlink{classegiw_3d5c719f15a5527a6c62c2a53160148e}{xdim}}
147\label{classegiw_3d5c719f15a5527a6c62c2a53160148e}
148
149\begin{CompactList}\small\item\em Dimension of the output. \item\end{CompactList}\item 
150\hypertarget{classegiw_c70d13d86e0d9f0acede3e1dc0368812}{
151int \hyperlink{classegiw_c70d13d86e0d9f0acede3e1dc0368812}{nPsi}}
152\label{classegiw_c70d13d86e0d9f0acede3e1dc0368812}
153
154\begin{CompactList}\small\item\em Dimension of the regressor. \item\end{CompactList}\item 
155\hypertarget{classepdf_74da992e3f5d598da8850b646b79b9d9}{
156\hyperlink{classRV}{RV} \hyperlink{classepdf_74da992e3f5d598da8850b646b79b9d9}{rv}}
157\label{classepdf_74da992e3f5d598da8850b646b79b9d9}
158
159\begin{CompactList}\small\item\em Identified of the random variable. \item\end{CompactList}\end{CompactItemize}
160
161
162\subsection{Detailed Description}
163Gauss-inverse-Wishart density stored in LD form.
164
165For $p$-variate densities, given rv.count() should be $p\times$ V.rows().
166
167The documentation for this class was generated from the following files:\begin{CompactItemize}
168\item 
169work/git/mixpp/bdm/stat/\hyperlink{libEF_8h}{libEF.h}\item 
170work/git/mixpp/bdm/stat/libEF.cpp\end{CompactItemize}
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