mgamma mEF mgamma_fix libEF.h egamma egamma mgamma::epdf epdf Internal epdf that arise by conditioning on rvc. double double mgamma::k k Constant $k$. mgamma_fix::condition condition set_parameters vec * vec* mgamma::_beta _beta cache of epdf.beta mgamma_fix::condition condition mgamma set_parameters RV RV mpdf::rv rv modeled random variable mpdf::_rv mprod::mprod mlnorm< sq_T >::samplecond mpdf::samplecond mlnorm< sq_T >::set_parameters set_parameters RV RV mpdf::rvc rvc random variable in condition mpdf::_rvc mprod::mprod epdf * epdf* mpdf::ep ep pointer to internal epdf mpdf::_epdf mpdf::evalcond mepdf::mepdf mlnorm< sq_T >::mlnorm mmix::mmix mpdf::samplecond set_parameters mgamma::mgamma (const RV &rv, const RV &rvc) mgamma const RV & rv const RV & rvc Constructor. _beta void void mgamma::set_parameters (double k) set_parameters double k Set value of k. _beta RV::count mpdf::ep k mpdf::rv mgamma_fix::set_parameters void void mgamma::condition (const vec &val) condition condition condition const vec & cond Update ep so that it represents this mpdf conditioned on rvc = cond. _beta k vec virtual vec mpdf::samplecond (const vec &cond, double &ll) samplecond const vec & cond double & ll Returns the required moment of the epdf. Returns a sample from the density conditioned on cond, $x \sim epdf(rv|cond)$. cond is numeric value of rv ll is a return value of log-likelihood of the sample. mpdf::condition mpdf::ep epdf::evalpdflog epdf::sample MPF< BM_T >::bayes PF::bayes mat virtual mat mpdf::samplecond (const vec &cond, vec &ll, int N) samplecond const vec & cond vec & ll int N Returns. N samples from the density conditioned on cond, $x \sim epdf(rv|cond)$. cond is numeric value of rv ll is a return value of log-likelihood of the sample. mpdf::condition RV::count mpdf::ep epdf::evalpdflog mpdf::rv epdf::sample double virtual double mpdf::evalcond (const vec &dt, const vec &cond) evalcond const vec & dt const vec & cond Shortcut for conditioning and evaluation of the internal epdf. In some cases, this operation can be implemented efficiently. mpdf::condition mpdf::ep epdf::eval PF::bayes RV RV mpdf::_rvc () _rvc access function mpdf::rvc merger::merger RV RV mpdf::_rv () _rv access function mpdf::rv mprod::mprod epdf & epdf& mpdf::_epdf () _epdf access function mpdf::ep Gamma random walk. Mean value, $\mu$, of this density is given by rvc . Standard deviation of the random walk is proportional to one $k$-th the mean. This is achieved by setting $\alpha=k$ and $\beta=k/\mu$.The standard deviation of the walk is then: $\mu/\sqrt(k)$. mgamma mpdf mEF mgamma_fix epdf rv RV mgamma epdf mpdf rv rvc ep egamma eEF mEF mgamma_beta mgamma_epdf mgamma_rv mgamma_rvc mgammacondition mgammaep mgammaepdf mgammaevalcond mgammak mgammamEF mgammamgamma mgammampdf mgammarv mgammarvc mgammasamplecond mgammasamplecond mgammaset_parameters mgamma~mpdf