[30] | 1 | \section{mgamma Class Reference} |
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| 2 | \label{classmgamma}\index{mgamma@{mgamma}} |
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| 3 | Gamma random walk. |
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| 4 | |
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| 5 | |
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| 6 | {\tt \#include $<$libEF.h$>$} |
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| 7 | |
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[33] | 8 | Inheritance diagram for mgamma:\nopagebreak |
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| 9 | \begin{figure}[H] |
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| 10 | \begin{center} |
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| 11 | \leavevmode |
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[91] | 12 | \includegraphics[width=58pt]{classmgamma__inherit__graph} |
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[33] | 13 | \end{center} |
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| 14 | \end{figure} |
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[30] | 15 | Collaboration diagram for mgamma:\nopagebreak |
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| 16 | \begin{figure}[H] |
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| 17 | \begin{center} |
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| 18 | \leavevmode |
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[91] | 19 | \includegraphics[width=76pt]{classmgamma__coll__graph} |
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[30] | 20 | \end{center} |
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| 21 | \end{figure} |
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| 22 | \subsection*{Public Member Functions} |
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| 23 | \begin{CompactItemize} |
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| 24 | \item |
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[33] | 25 | {\bf mgamma} (const {\bf RV} \&{\bf rv}, const {\bf RV} \&{\bf rvc})\label{classmgamma_af43e61b86900c0398d5c0ffc83b94e6} |
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[30] | 26 | |
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| 27 | \begin{CompactList}\small\item\em Constructor. \item\end{CompactList}\item |
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[79] | 28 | void {\bf set\_\-parameters} (double {\bf k})\label{classmgamma_a9d646cf758a70126dde7c48790b6e94} |
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[30] | 29 | |
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[33] | 30 | \begin{CompactList}\small\item\em Set value of {\tt k}. \item\end{CompactList}\item |
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[30] | 31 | vec {\bf samplecond} (vec \&cond, double \&lik)\label{classmgamma_9f40dc43885085fad8e3d6652b79e139} |
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| 32 | |
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| 33 | \begin{CompactList}\small\item\em Generate one sample of the posterior. \item\end{CompactList}\item |
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| 34 | mat {\bf samplecond} (vec \&cond, vec \&lik, int n)\label{classmgamma_e9d52749793f40aad85b70c6db4435ae} |
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| 35 | |
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| 36 | \begin{CompactList}\small\item\em Generate matrix of samples of the posterior. \item\end{CompactList}\item |
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[33] | 37 | void {\bf condition} (const vec \&val)\label{classmgamma_a61094c9f7a2d64ea77b130cbc031f97} |
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[30] | 38 | |
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[33] | 39 | \begin{CompactList}\small\item\em Update {\tt ep} so that it represents this \doxyref{mpdf}{p.}{classmpdf} conditioned on {\tt rvc} = cond. \item\end{CompactList}\item |
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| 40 | virtual double {\bf evalcond} (const vec \&dt, const vec \&cond)\label{classmpdf_80b738ece5bd4f8c4edaee4b38906f91} |
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[30] | 41 | |
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[33] | 42 | \begin{CompactList}\small\item\em Shortcut for conditioning and evaluation of the internal \doxyref{epdf}{p.}{classepdf}. In some cases, this operation can be implemented efficiently. \item\end{CompactList}\item |
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| 43 | {\bf RV} {\bf \_\-rvc} ()\label{classmpdf_ec9c850305984582548e8deb64f0ffe8} |
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[30] | 44 | |
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[33] | 45 | \begin{CompactList}\small\item\em access function \item\end{CompactList}\item |
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| 46 | {\bf epdf} \& {\bf \_\-epdf} ()\label{classmpdf_e17780ee5b2cfe05922a6c56af1462f8} |
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| 47 | |
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| 48 | \begin{CompactList}\small\item\em access function \item\end{CompactList}\end{CompactItemize} |
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| 49 | \subsection*{Protected Attributes} |
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| 50 | \begin{CompactItemize} |
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| 51 | \item |
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[79] | 52 | {\bf egamma} {\bf epdf}\label{classmgamma_612dbf35c770a780027619aaac2c443e} |
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| 53 | |
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| 54 | \begin{CompactList}\small\item\em Internal \doxyref{epdf}{p.}{classepdf} that arise by conditioning on {\tt rvc}. \item\end{CompactList}\item |
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| 55 | double {\bf k}\label{classmgamma_43f733cce0245a52363d566099add687} |
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| 56 | |
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[91] | 57 | \begin{CompactList}\small\item\em Constant $k$. \item\end{CompactList}\item |
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[79] | 58 | vec $\ast$ {\bf \_\-beta}\label{classmgamma_5e90652837448bcc29707e7412f99691} |
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| 59 | |
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| 60 | \begin{CompactList}\small\item\em cache of epdf.beta \item\end{CompactList}\item |
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[33] | 61 | {\bf RV} {\bf rv}\label{classmpdf_f6687c07ff07d47812dd565368ca59eb} |
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| 62 | |
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| 63 | \begin{CompactList}\small\item\em modeled random variable \item\end{CompactList}\item |
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| 64 | {\bf RV} {\bf rvc}\label{classmpdf_acb7dda792b3cd5576f39fa3129abbab} |
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| 65 | |
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| 66 | \begin{CompactList}\small\item\em random variable in condition \item\end{CompactList}\item |
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| 67 | {\bf epdf} $\ast$ {\bf ep}\label{classmpdf_7aa894208a32f3487827df6d5054424c} |
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| 68 | |
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| 69 | \begin{CompactList}\small\item\em pointer to internal \doxyref{epdf}{p.}{classepdf} \item\end{CompactList}\end{CompactItemize} |
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| 70 | |
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| 71 | |
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[30] | 72 | \subsection{Detailed Description} |
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| 73 | Gamma random walk. |
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| 74 | |
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[91] | 75 | Mean 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 $\beta=k/\mu$. |
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[30] | 76 | |
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[33] | 77 | The standard deviation of the walk is then: $\mu/\sqrt(k)$. |
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[30] | 78 | |
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| 79 | The documentation for this class was generated from the following files:\begin{CompactItemize} |
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| 80 | \item |
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[145] | 81 | work/git/mixpp/bdm/stat/{\bf libEF.h}\item |
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| 82 | work/git/mixpp/bdm/stat/libEF.cpp\end{CompactItemize} |
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