[99] | 1 | \section{mgamma\_\-fix Class Reference} |
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| 2 | \label{classmgamma__fix}\index{mgamma\_\-fix@{mgamma\_\-fix}} |
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| 3 | Gamma random walk around a fixed point. |
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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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| 8 | Inheritance diagram for mgamma\_\-fix:\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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| 12 | \includegraphics[width=58pt]{classmgamma__fix__inherit__graph} |
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| 13 | \end{center} |
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| 14 | \end{figure} |
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| 15 | Collaboration diagram for mgamma\_\-fix:\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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| 19 | \includegraphics[width=76pt]{classmgamma__fix__coll__graph} |
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| 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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| 25 | {\bf mgamma\_\-fix} (const {\bf RV} \&{\bf rv}, const {\bf RV} \&{\bf rvc})\label{classmgamma__fix_b92c3d2e5fd0381033a072e5ef3bcf80} |
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| 26 | |
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| 27 | \begin{CompactList}\small\item\em Constructor. \item\end{CompactList}\item |
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| 28 | void {\bf set\_\-parameters} (double k0, vec ref0, double l0)\label{classmgamma__fix_ec6f846896749e27cb7be9fa48dd1cb1} |
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| 29 | |
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| 30 | \begin{CompactList}\small\item\em Set value of {\tt k}. \item\end{CompactList}\item |
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| 31 | void {\bf condition} (const vec \&val)\label{classmgamma__fix_6ea3931eec7b7da7b693e45981052460} |
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| 32 | |
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| 33 | \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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| 34 | void {\bf set\_\-parameters} (double {\bf k})\label{classmgamma_a9d646cf758a70126dde7c48790b6e94} |
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| 35 | |
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| 36 | \begin{CompactList}\small\item\em Set value of {\tt k}. \item\end{CompactList}\item |
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| 37 | vec {\bf samplecond} (vec \&cond, double \&lik)\label{classmgamma_9f40dc43885085fad8e3d6652b79e139} |
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| 38 | |
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| 39 | \begin{CompactList}\small\item\em Generate one sample of the posterior. \item\end{CompactList}\item |
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| 40 | mat {\bf samplecond} (vec \&cond, vec \&lik, int n)\label{classmgamma_e9d52749793f40aad85b70c6db4435ae} |
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| 41 | |
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| 42 | \begin{CompactList}\small\item\em Generate matrix of samples of the posterior. \item\end{CompactList}\item |
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| 43 | virtual double {\bf evalcond} (const vec \&dt, const vec \&cond)\label{classmpdf_80b738ece5bd4f8c4edaee4b38906f91} |
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| 44 | |
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| 45 | \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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| 46 | {\bf RV} {\bf \_\-rvc} ()\label{classmpdf_ec9c850305984582548e8deb64f0ffe8} |
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| 47 | |
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| 48 | \begin{CompactList}\small\item\em access function \item\end{CompactList}\item |
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| 49 | {\bf epdf} \& {\bf \_\-epdf} ()\label{classmpdf_e17780ee5b2cfe05922a6c56af1462f8} |
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| 50 | |
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| 51 | \begin{CompactList}\small\item\em access function \item\end{CompactList}\end{CompactItemize} |
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| 52 | \subsection*{Protected Attributes} |
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| 53 | \begin{CompactItemize} |
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| 54 | \item |
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| 55 | double {\bf l}\label{classmgamma__fix_3f48c09caddc298901ad75fe7c0529f6} |
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| 56 | |
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| 57 | \begin{CompactList}\small\item\em parameter l \item\end{CompactList}\item |
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| 58 | vec {\bf refl}\label{classmgamma__fix_81ce49029ecc385418619b200dcafeb0} |
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| 59 | |
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| 60 | \begin{CompactList}\small\item\em reference vector \item\end{CompactList}\item |
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| 61 | {\bf egamma} {\bf epdf}\label{classmgamma_612dbf35c770a780027619aaac2c443e} |
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| 62 | |
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| 63 | \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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| 64 | double {\bf k}\label{classmgamma_43f733cce0245a52363d566099add687} |
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| 65 | |
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| 66 | \begin{CompactList}\small\item\em Constant $k$. \item\end{CompactList}\item |
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| 67 | vec $\ast$ {\bf \_\-beta}\label{classmgamma_5e90652837448bcc29707e7412f99691} |
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| 68 | |
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| 69 | \begin{CompactList}\small\item\em cache of epdf.beta \item\end{CompactList}\item |
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| 70 | {\bf RV} {\bf rv}\label{classmpdf_f6687c07ff07d47812dd565368ca59eb} |
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| 71 | |
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| 72 | \begin{CompactList}\small\item\em modeled random variable \item\end{CompactList}\item |
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| 73 | {\bf RV} {\bf rvc}\label{classmpdf_acb7dda792b3cd5576f39fa3129abbab} |
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| 74 | |
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| 75 | \begin{CompactList}\small\item\em random variable in condition \item\end{CompactList}\item |
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| 76 | {\bf epdf} $\ast$ {\bf ep}\label{classmpdf_7aa894208a32f3487827df6d5054424c} |
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| 77 | |
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| 78 | \begin{CompactList}\small\item\em pointer to internal \doxyref{epdf}{p.}{classepdf} \item\end{CompactList}\end{CompactItemize} |
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| 79 | |
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| 80 | |
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| 81 | \subsection{Detailed Description} |
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| 82 | Gamma random walk around a fixed point. |
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| 83 | |
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| 84 | 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 \[ \mu = \mu_{t-1} ^{l} p^{1-l}\] |
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| 85 | |
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| 86 | 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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| 87 | |
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| 88 | The standard deviation of the walk is then: $\mu/\sqrt(k)$. |
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| 89 | |
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| 90 | The documentation for this class was generated from the following file:\begin{CompactItemize} |
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| 91 | \item |
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[145] | 92 | work/git/mixpp/bdm/stat/{\bf libEF.h}\end{CompactItemize} |
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