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1\hypertarget{classbdm_1_1mgamma}{
2\section{bdm::mgamma Class Reference}
3\label{classbdm_1_1mgamma}\index{bdm::mgamma@{bdm::mgamma}}
4}
5Gamma random walk. 
6
7
8{\tt \#include $<$libEF.h$>$}
9
10Inheritance diagram for bdm::mgamma:\nopagebreak
11\begin{figure}[H]
12\begin{center}
13\leavevmode
14\includegraphics[width=75pt]{classbdm_1_1mgamma__inherit__graph}
15\end{center}
16\end{figure}
17Collaboration diagram for bdm::mgamma:\nopagebreak
18\begin{figure}[H]
19\begin{center}
20\leavevmode
21\includegraphics[height=400pt]{classbdm_1_1mgamma__coll__graph}
22\end{center}
23\end{figure}
24\subsection*{Public Member Functions}
25\begin{CompactItemize}
26\item 
27\hypertarget{classbdm_1_1mgamma_2f6425cd966191b0be4c6ea91a40b6d9}{
28\hyperlink{classbdm_1_1mgamma_2f6425cd966191b0be4c6ea91a40b6d9}{mgamma} (const \hyperlink{classbdm_1_1RV}{RV} \&\hyperlink{classbdm_1_1mpdf_9bcfb45435d30983f436d41c298cbb51}{rv}, const \hyperlink{classbdm_1_1RV}{RV} \&\hyperlink{classbdm_1_1mpdf_5a5f08950daa08b85b01ddf4e1c36288}{rvc})}
29\label{classbdm_1_1mgamma_2f6425cd966191b0be4c6ea91a40b6d9}
30
31\begin{CompactList}\small\item\em Constructor. \item\end{CompactList}\item 
32\hypertarget{classbdm_1_1mgamma_0b486f7e52a3d8a39adbcbd325461c0d}{
33void \hyperlink{classbdm_1_1mgamma_0b486f7e52a3d8a39adbcbd325461c0d}{set\_\-parameters} (double \hyperlink{classbdm_1_1mgamma_b20cf88cca1fe9b0b8f2a412608bfd09}{k})}
34\label{classbdm_1_1mgamma_0b486f7e52a3d8a39adbcbd325461c0d}
35
36\begin{CompactList}\small\item\em Set value of {\tt k}. \item\end{CompactList}\item 
37\hypertarget{classbdm_1_1mgamma_8996500f1885e39cde30221b20900bff}{
38void \hyperlink{classbdm_1_1mgamma_8996500f1885e39cde30221b20900bff}{condition} (const vec \&val)}
39\label{classbdm_1_1mgamma_8996500f1885e39cde30221b20900bff}
40
41\begin{CompactList}\small\item\em Update {\tt ep} so that it represents this \hyperlink{classbdm_1_1mpdf}{mpdf} conditioned on {\tt rvc} = cond. \item\end{CompactList}\item 
42virtual vec \hyperlink{classbdm_1_1mpdf_e4848a428d8ef0549c6e4a9ed386d9f2}{samplecond} (const vec \&cond, double \&ll)
43\begin{CompactList}\small\item\em Returns a sample from the density conditioned on {\tt cond}, $x \sim epdf(rv|cond)$. \item\end{CompactList}\item 
44virtual mat \hyperlink{classbdm_1_1mpdf_ee26963a637b2ea1fb1933652981e652}{samplecond\_\-m} (const vec \&cond, vec \&ll, int N)
45\begin{CompactList}\small\item\em Returns. \item\end{CompactList}\item 
46\hypertarget{classbdm_1_1mpdf_6336a8a72462e2a56a3989a220f18b1b}{
47virtual double \hyperlink{classbdm_1_1mpdf_6336a8a72462e2a56a3989a220f18b1b}{evallogcond} (const vec \&dt, const vec \&cond)}
48\label{classbdm_1_1mpdf_6336a8a72462e2a56a3989a220f18b1b}
49
50\begin{CompactList}\small\item\em Shortcut for conditioning and evaluation of the internal \hyperlink{classbdm_1_1epdf}{epdf}. In some cases, this operation can be implemented efficiently. \item\end{CompactList}\item 
51\hypertarget{classbdm_1_1mpdf_0b0ed1ed663071bb7cf4a1349eb94fcb}{
52virtual vec \hyperlink{classbdm_1_1mpdf_0b0ed1ed663071bb7cf4a1349eb94fcb}{evallogcond\_\-m} (const mat \&Dt, const vec \&cond)}
53\label{classbdm_1_1mpdf_0b0ed1ed663071bb7cf4a1349eb94fcb}
54
55\begin{CompactList}\small\item\em Matrix version of evallogcond. \item\end{CompactList}\item 
56\hypertarget{classbdm_1_1mpdf_b3aba7311038bf990d706a64cab60cf8}{
57\hyperlink{classbdm_1_1RV}{RV} \hyperlink{classbdm_1_1mpdf_b3aba7311038bf990d706a64cab60cf8}{\_\-rvc} () const }
58\label{classbdm_1_1mpdf_b3aba7311038bf990d706a64cab60cf8}
59
60\begin{CompactList}\small\item\em access function \item\end{CompactList}\item 
61\hypertarget{classbdm_1_1mpdf_222d5280e309c5a053ba73841e98c151}{
62\hyperlink{classbdm_1_1RV}{RV} \hyperlink{classbdm_1_1mpdf_222d5280e309c5a053ba73841e98c151}{\_\-rv} () const }
63\label{classbdm_1_1mpdf_222d5280e309c5a053ba73841e98c151}
64
65\begin{CompactList}\small\item\em access function \item\end{CompactList}\item 
66\hypertarget{classbdm_1_1mpdf_1892fe3933488942253679f068e9e7f6}{
67\hyperlink{classbdm_1_1epdf}{epdf} \& \hyperlink{classbdm_1_1mpdf_1892fe3933488942253679f068e9e7f6}{\_\-epdf} ()}
68\label{classbdm_1_1mpdf_1892fe3933488942253679f068e9e7f6}
69
70\begin{CompactList}\small\item\em access function \item\end{CompactList}\item 
71\hypertarget{classbdm_1_1mpdf_05e843fd11c410a99dad2b88c55aca80}{
72\hyperlink{classbdm_1_1epdf}{epdf} $\ast$ \hyperlink{classbdm_1_1mpdf_05e843fd11c410a99dad2b88c55aca80}{\_\-e} ()}
73\label{classbdm_1_1mpdf_05e843fd11c410a99dad2b88c55aca80}
74
75\begin{CompactList}\small\item\em access function \item\end{CompactList}\end{CompactItemize}
76\subsection*{Protected Attributes}
77\begin{CompactItemize}
78\item 
79\hypertarget{classbdm_1_1mgamma_bdc9f1e9e03c09e91103fee269864438}{
80\hyperlink{classbdm_1_1egamma}{egamma} \hyperlink{classbdm_1_1mgamma_bdc9f1e9e03c09e91103fee269864438}{epdf}}
81\label{classbdm_1_1mgamma_bdc9f1e9e03c09e91103fee269864438}
82
83\begin{CompactList}\small\item\em Internal \hyperlink{classbdm_1_1epdf}{epdf} that arise by conditioning on {\tt rvc}. \item\end{CompactList}\item 
84\hypertarget{classbdm_1_1mgamma_b20cf88cca1fe9b0b8f2a412608bfd09}{
85double \hyperlink{classbdm_1_1mgamma_b20cf88cca1fe9b0b8f2a412608bfd09}{k}}
86\label{classbdm_1_1mgamma_b20cf88cca1fe9b0b8f2a412608bfd09}
87
88\begin{CompactList}\small\item\em Constant $k$. \item\end{CompactList}\item 
89\hypertarget{classbdm_1_1mgamma_f6a652ce70fa2eb4d2c7ba6d5a6ae343}{
90vec $\ast$ \hyperlink{classbdm_1_1mgamma_f6a652ce70fa2eb4d2c7ba6d5a6ae343}{\_\-beta}}
91\label{classbdm_1_1mgamma_f6a652ce70fa2eb4d2c7ba6d5a6ae343}
92
93\begin{CompactList}\small\item\em cache of epdf.beta \item\end{CompactList}\item 
94\hypertarget{classbdm_1_1mpdf_9bcfb45435d30983f436d41c298cbb51}{
95\hyperlink{classbdm_1_1RV}{RV} \hyperlink{classbdm_1_1mpdf_9bcfb45435d30983f436d41c298cbb51}{rv}}
96\label{classbdm_1_1mpdf_9bcfb45435d30983f436d41c298cbb51}
97
98\begin{CompactList}\small\item\em modeled random variable \item\end{CompactList}\item 
99\hypertarget{classbdm_1_1mpdf_5a5f08950daa08b85b01ddf4e1c36288}{
100\hyperlink{classbdm_1_1RV}{RV} \hyperlink{classbdm_1_1mpdf_5a5f08950daa08b85b01ddf4e1c36288}{rvc}}
101\label{classbdm_1_1mpdf_5a5f08950daa08b85b01ddf4e1c36288}
102
103\begin{CompactList}\small\item\em random variable in condition \item\end{CompactList}\item 
104\hypertarget{classbdm_1_1mpdf_5eea43c56d38e4441bfb30270db949c0}{
105\hyperlink{classbdm_1_1epdf}{epdf} $\ast$ \hyperlink{classbdm_1_1mpdf_5eea43c56d38e4441bfb30270db949c0}{ep}}
106\label{classbdm_1_1mpdf_5eea43c56d38e4441bfb30270db949c0}
107
108\begin{CompactList}\small\item\em pointer to internal \hyperlink{classbdm_1_1epdf}{epdf} \item\end{CompactList}\end{CompactItemize}
109
110
111\subsection{Detailed Description}
112Gamma random walk.
113
114Mean value, $\mu$, of this density is given by {\tt rvc} . Standard deviation of the random walk is proportional to one $k$-th the mean. This is achieved by setting $\alpha=k$ and $\beta=k/\mu$.
115
116The standard deviation of the walk is then: $\mu/\sqrt(k)$.
117
118\subsection{Member Function Documentation}
119\hypertarget{classbdm_1_1mpdf_e4848a428d8ef0549c6e4a9ed386d9f2}{
120\index{bdm::mgamma@{bdm::mgamma}!samplecond@{samplecond}}
121\index{samplecond@{samplecond}!bdm::mgamma@{bdm::mgamma}}
122\subsubsection[samplecond]{\setlength{\rightskip}{0pt plus 5cm}virtual vec bdm::mpdf::samplecond (const vec \& {\em cond}, \/  double \& {\em ll})\hspace{0.3cm}{\tt  \mbox{[}inline, virtual, inherited\mbox{]}}}}
123\label{classbdm_1_1mpdf_e4848a428d8ef0549c6e4a9ed386d9f2}
124
125
126Returns a sample from the density conditioned on {\tt cond}, $x \sim epdf(rv|cond)$.
127
128\begin{Desc}
129\item[Parameters:]
130\begin{description}
131\item[{\em cond}]is numeric value of {\tt rv} \item[{\em ll}]is a return value of log-likelihood of the sample. \end{description}
132\end{Desc}
133
134
135Reimplemented in \hyperlink{classbdm_1_1mprod_1a37c2aaba8bde7fce5351c39b6e1168}{bdm::mprod}.
136
137References bdm::mpdf::condition(), bdm::mpdf::ep, bdm::epdf::evallog(), and bdm::epdf::sample().
138
139Referenced by bdm::MPF$<$ BM\_\-T $>$::bayes(), bdm::PF::bayes(), and bdm::ArxDS::step().\hypertarget{classbdm_1_1mpdf_ee26963a637b2ea1fb1933652981e652}{
140\index{bdm::mgamma@{bdm::mgamma}!samplecond\_\-m@{samplecond\_\-m}}
141\index{samplecond\_\-m@{samplecond\_\-m}!bdm::mgamma@{bdm::mgamma}}
142\subsubsection[samplecond\_\-m]{\setlength{\rightskip}{0pt plus 5cm}virtual mat bdm::mpdf::samplecond\_\-m (const vec \& {\em cond}, \/  vec \& {\em ll}, \/  int {\em N})\hspace{0.3cm}{\tt  \mbox{[}inline, virtual, inherited\mbox{]}}}}
143\label{classbdm_1_1mpdf_ee26963a637b2ea1fb1933652981e652}
144
145
146Returns.
147
148\begin{Desc}
149\item[Parameters:]
150\begin{description}
151\item[{\em N}]samples from the density conditioned on {\tt cond}, $x \sim epdf(rv|cond)$. \item[{\em cond}]is numeric value of {\tt rv} \item[{\em ll}]is a return value of log-likelihood of the sample. \end{description}
152\end{Desc}
153
154
155References bdm::mpdf::condition(), bdm::RV::count(), bdm::mpdf::ep, bdm::epdf::evallog(), bdm::mpdf::rv, and bdm::epdf::sample().
156
157The documentation for this class was generated from the following files:\begin{CompactItemize}
158\item 
159\hyperlink{libEF_8h}{libEF.h}\item 
160libEF.cpp\end{CompactItemize}
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