\hypertarget{classbdm_1_1migamma__fix}{
\section{bdm::migamma\_\-fix Class Reference}
\label{classbdm_1_1migamma__fix}\index{bdm::migamma\_\-fix@{bdm::migamma\_\-fix}}
}
Inverse-Gamma random walk around a fixed point.  


{\tt \#include $<$libEF.h$>$}

Inheritance diagram for bdm::migamma\_\-fix:\nopagebreak
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\begin{center}
\leavevmode
\includegraphics[width=76pt]{classbdm_1_1migamma__fix__inherit__graph}
\end{center}
\end{figure}
Collaboration diagram for bdm::migamma\_\-fix:\nopagebreak
\begin{figure}[H]
\begin{center}
\leavevmode
\includegraphics[height=400pt]{classbdm_1_1migamma__fix__coll__graph}
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\subsection*{Public Member Functions}
\begin{CompactItemize}
\item 
\hypertarget{classbdm_1_1migamma__fix_3c6aacebccbe6d73f8d442e82d3cb53a}{
\hyperlink{classbdm_1_1migamma__fix_3c6aacebccbe6d73f8d442e82d3cb53a}{migamma\_\-fix} (const \hyperlink{classbdm_1_1RV}{RV} \&\hyperlink{classbdm_1_1mpdf_9bcfb45435d30983f436d41c298cbb51}{rv}, const \hyperlink{classbdm_1_1RV}{RV} \&\hyperlink{classbdm_1_1mpdf_5a5f08950daa08b85b01ddf4e1c36288}{rvc})}
\label{classbdm_1_1migamma__fix_3c6aacebccbe6d73f8d442e82d3cb53a}

\begin{CompactList}\small\item\em Constructor. \item\end{CompactList}\item 
\hypertarget{classbdm_1_1migamma__fix_17f9ce1068660a4e8c05173bef7d7440}{
void \hyperlink{classbdm_1_1migamma__fix_17f9ce1068660a4e8c05173bef7d7440}{set\_\-parameters} (double k0, vec ref0, double l0)}
\label{classbdm_1_1migamma__fix_17f9ce1068660a4e8c05173bef7d7440}

\begin{CompactList}\small\item\em Set value of {\tt k}. \item\end{CompactList}\item 
\hypertarget{classbdm_1_1migamma__fix_cfbabd293795d44aae1b7c874e57c4b8}{
void \hyperlink{classbdm_1_1migamma__fix_cfbabd293795d44aae1b7c874e57c4b8}{condition} (const vec \&val)}
\label{classbdm_1_1migamma__fix_cfbabd293795d44aae1b7c874e57c4b8}

\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 
\hypertarget{classbdm_1_1migamma_1d7023b1565551d0260eb1ba832bebaf}{
void \hyperlink{classbdm_1_1migamma_1d7023b1565551d0260eb1ba832bebaf}{set\_\-parameters} (double k0)}
\label{classbdm_1_1migamma_1d7023b1565551d0260eb1ba832bebaf}

\begin{CompactList}\small\item\em Set value of {\tt k}. \item\end{CompactList}\item 
virtual vec \hyperlink{classbdm_1_1mpdf_f0c1db6fcbb3aae2dd6123884457a367}{samplecond} (const vec \&cond)
\begin{CompactList}\small\item\em Returns a sample from the density conditioned on {\tt cond}, $x \sim epdf(rv|cond)$. \item\end{CompactList}\item 
virtual mat \hyperlink{classbdm_1_1mpdf_ee26963a637b2ea1fb1933652981e652}{samplecond\_\-m} (const vec \&cond, vec \&ll, int N)
\begin{CompactList}\small\item\em Returns. \item\end{CompactList}\item 
\hypertarget{classbdm_1_1mpdf_6336a8a72462e2a56a3989a220f18b1b}{
virtual double \hyperlink{classbdm_1_1mpdf_6336a8a72462e2a56a3989a220f18b1b}{evallogcond} (const vec \&dt, const vec \&cond)}
\label{classbdm_1_1mpdf_6336a8a72462e2a56a3989a220f18b1b}

\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 
\hypertarget{classbdm_1_1mpdf_0b0ed1ed663071bb7cf4a1349eb94fcb}{
virtual vec \hyperlink{classbdm_1_1mpdf_0b0ed1ed663071bb7cf4a1349eb94fcb}{evallogcond\_\-m} (const mat \&Dt, const vec \&cond)}
\label{classbdm_1_1mpdf_0b0ed1ed663071bb7cf4a1349eb94fcb}

\begin{CompactList}\small\item\em Matrix version of evallogcond. \item\end{CompactList}\item 
\hypertarget{classbdm_1_1mpdf_b3aba7311038bf990d706a64cab60cf8}{
\hyperlink{classbdm_1_1RV}{RV} \hyperlink{classbdm_1_1mpdf_b3aba7311038bf990d706a64cab60cf8}{\_\-rvc} () const }
\label{classbdm_1_1mpdf_b3aba7311038bf990d706a64cab60cf8}

\begin{CompactList}\small\item\em access function \item\end{CompactList}\item 
\hypertarget{classbdm_1_1mpdf_222d5280e309c5a053ba73841e98c151}{
\hyperlink{classbdm_1_1RV}{RV} \hyperlink{classbdm_1_1mpdf_222d5280e309c5a053ba73841e98c151}{\_\-rv} () const }
\label{classbdm_1_1mpdf_222d5280e309c5a053ba73841e98c151}

\begin{CompactList}\small\item\em access function \item\end{CompactList}\item 
\hypertarget{classbdm_1_1mpdf_1892fe3933488942253679f068e9e7f6}{
\hyperlink{classbdm_1_1epdf}{epdf} \& \hyperlink{classbdm_1_1mpdf_1892fe3933488942253679f068e9e7f6}{\_\-epdf} ()}
\label{classbdm_1_1mpdf_1892fe3933488942253679f068e9e7f6}

\begin{CompactList}\small\item\em access function \item\end{CompactList}\item 
\hypertarget{classbdm_1_1mpdf_05e843fd11c410a99dad2b88c55aca80}{
\hyperlink{classbdm_1_1epdf}{epdf} $\ast$ \hyperlink{classbdm_1_1mpdf_05e843fd11c410a99dad2b88c55aca80}{\_\-e} ()}
\label{classbdm_1_1mpdf_05e843fd11c410a99dad2b88c55aca80}

\begin{CompactList}\small\item\em access function \item\end{CompactList}\end{CompactItemize}
\subsection*{Protected Attributes}
\begin{CompactItemize}
\item 
\hypertarget{classbdm_1_1migamma__fix_e1c344accac36d7ccc3ffa502e8d2f4e}{
double \hyperlink{classbdm_1_1migamma__fix_e1c344accac36d7ccc3ffa502e8d2f4e}{l}}
\label{classbdm_1_1migamma__fix_e1c344accac36d7ccc3ffa502e8d2f4e}

\begin{CompactList}\small\item\em parameter l \item\end{CompactList}\item 
\hypertarget{classbdm_1_1migamma__fix_5d453e5a2bdfc9a16c8acb8842dc9780}{
vec \hyperlink{classbdm_1_1migamma__fix_5d453e5a2bdfc9a16c8acb8842dc9780}{refl}}
\label{classbdm_1_1migamma__fix_5d453e5a2bdfc9a16c8acb8842dc9780}

\begin{CompactList}\small\item\em reference vector \item\end{CompactList}\item 
\hypertarget{classbdm_1_1migamma_a31b39d4179551b593c9e0d7d756783a}{
\hyperlink{classbdm_1_1eigamma}{eigamma} \hyperlink{classbdm_1_1migamma_a31b39d4179551b593c9e0d7d756783a}{epdf}}
\label{classbdm_1_1migamma_a31b39d4179551b593c9e0d7d756783a}

\begin{CompactList}\small\item\em Internal \hyperlink{classbdm_1_1epdf}{epdf} that arise by conditioning on {\tt rvc}. \item\end{CompactList}\item 
\hypertarget{classbdm_1_1migamma_dc56bc9da542e0103ec16b9be8e5e38c}{
double \hyperlink{classbdm_1_1migamma_dc56bc9da542e0103ec16b9be8e5e38c}{k}}
\label{classbdm_1_1migamma_dc56bc9da542e0103ec16b9be8e5e38c}

\begin{CompactList}\small\item\em Constant $k$. \item\end{CompactList}\item 
\hypertarget{classbdm_1_1migamma_4825c0ef11a148bad9b802a496f56f96}{
vec $\ast$ \hyperlink{classbdm_1_1migamma_4825c0ef11a148bad9b802a496f56f96}{\_\-beta}}
\label{classbdm_1_1migamma_4825c0ef11a148bad9b802a496f56f96}

\begin{CompactList}\small\item\em cache of epdf.beta \item\end{CompactList}\item 
\hypertarget{classbdm_1_1migamma_b6c265b132ff79963bf51dff4c3ef252}{
vec $\ast$ \hyperlink{classbdm_1_1migamma_b6c265b132ff79963bf51dff4c3ef252}{\_\-alpha}}
\label{classbdm_1_1migamma_b6c265b132ff79963bf51dff4c3ef252}

\begin{CompactList}\small\item\em chaceh of epdf.alpha \item\end{CompactList}\item 
\hypertarget{classbdm_1_1mpdf_9bcfb45435d30983f436d41c298cbb51}{
\hyperlink{classbdm_1_1RV}{RV} \hyperlink{classbdm_1_1mpdf_9bcfb45435d30983f436d41c298cbb51}{rv}}
\label{classbdm_1_1mpdf_9bcfb45435d30983f436d41c298cbb51}

\begin{CompactList}\small\item\em modeled random variable \item\end{CompactList}\item 
\hypertarget{classbdm_1_1mpdf_5a5f08950daa08b85b01ddf4e1c36288}{
\hyperlink{classbdm_1_1RV}{RV} \hyperlink{classbdm_1_1mpdf_5a5f08950daa08b85b01ddf4e1c36288}{rvc}}
\label{classbdm_1_1mpdf_5a5f08950daa08b85b01ddf4e1c36288}

\begin{CompactList}\small\item\em random variable in condition \item\end{CompactList}\item 
\hypertarget{classbdm_1_1mpdf_5eea43c56d38e4441bfb30270db949c0}{
\hyperlink{classbdm_1_1epdf}{epdf} $\ast$ \hyperlink{classbdm_1_1mpdf_5eea43c56d38e4441bfb30270db949c0}{ep}}
\label{classbdm_1_1mpdf_5eea43c56d38e4441bfb30270db949c0}

\begin{CompactList}\small\item\em pointer to internal \hyperlink{classbdm_1_1epdf}{epdf} \item\end{CompactList}\end{CompactItemize}


\subsection{Detailed Description}
Inverse-Gamma random walk around a fixed point. 

Mean value, $\mu$, of this density is given by a geometric combination of {\tt rvc} and given fixed point, $p$. $l$ is the coefficient of the geometric combimation \[ \mu = \mu_{t-1} ^{l} p^{1-l}\]

==== Check == vv = Standard deviation of the random walk is proportional to one $k$-th the mean. This is achieved by setting $\alpha=k$ and $\beta=k/\mu$.

The standard deviation of the walk is then: $\mu/\sqrt(k)$. 

\subsection{Member Function Documentation}
\hypertarget{classbdm_1_1mpdf_f0c1db6fcbb3aae2dd6123884457a367}{
\index{bdm::migamma\_\-fix@{bdm::migamma\_\-fix}!samplecond@{samplecond}}
\index{samplecond@{samplecond}!bdm::migamma_fix@{bdm::migamma\_\-fix}}
\subsubsection[samplecond]{\setlength{\rightskip}{0pt plus 5cm}virtual vec bdm::mpdf::samplecond (const vec \& {\em cond})\hspace{0.3cm}{\tt  \mbox{[}inline, virtual, inherited\mbox{]}}}}
\label{classbdm_1_1mpdf_f0c1db6fcbb3aae2dd6123884457a367}


Returns a sample from the density conditioned on {\tt cond}, $x \sim epdf(rv|cond)$. 

\begin{Desc}
\item[Parameters:]
\begin{description}
\item[{\em cond}]is numeric value of {\tt rv} \end{description}
\end{Desc}


References bdm::mpdf::condition(), bdm::mpdf::ep, and bdm::epdf::sample().

Referenced by bdm::MPF$<$ BM\_\-T $>$::bayes(), bdm::PF::bayes(), and bdm::ArxDS::step().\hypertarget{classbdm_1_1mpdf_ee26963a637b2ea1fb1933652981e652}{
\index{bdm::migamma\_\-fix@{bdm::migamma\_\-fix}!samplecond\_\-m@{samplecond\_\-m}}
\index{samplecond\_\-m@{samplecond\_\-m}!bdm::migamma_fix@{bdm::migamma\_\-fix}}
\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{]}}}}
\label{classbdm_1_1mpdf_ee26963a637b2ea1fb1933652981e652}


Returns. 

\begin{Desc}
\item[Parameters:]
\begin{description}
\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}
\end{Desc}


References bdm::mpdf::condition(), bdm::RV::count(), bdm::mpdf::ep, bdm::epdf::evallog(), bdm::mpdf::rv, and bdm::epdf::sample().

The documentation for this class was generated from the following file:\begin{CompactItemize}
\item 
\hyperlink{libEF_8h}{libEF.h}\end{CompactItemize}