mlnorm mEF libEF.h class sq_T sq_T enorm< sq_T > enorm<sq_T> mlnorm< sq_T >::epdf epdf Internal epdf that arise by conditioning on rvc. mat mat mlnorm< sq_T >::A A vec & vec& mlnorm< sq_T >::_mu _mu mlnorm< sq_T >::mlnorm (RV &rv, RV &rvc) mlnorm RV & rv RV & rvc Constructor. mpdf::ep void void mlnorm< sq_T >::set_parameters (const mat &A, const sq_T &R) set_parameters const mat & A const sq_T & R Set A and R. RV::count mpdf::rv vec vec mlnorm< sq_T >::samplecond (vec &cond, double &lik) samplecond vec & cond double & lik Generate one sample of the posterior. mlnorm< sq_T >::condition mat mat mlnorm< sq_T >::samplecond (vec &cond, vec &lik, int n) samplecond vec & cond vec & lik int n Generate matrix of samples of the posterior. mlnorm< sq_T >::condition RV::count mpdf::rv void void mlnorm< sq_T >::condition (vec &cond) condition vec & cond Set value of rvc . Result of this operation is stored in epdf use function _ep to access it. mlnorm< sq_T >::samplecond 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 void virtual void mpdf::condition (const vec &cond) condition condition condition condition const vec & cond Update ep so that it represents this mpdf conditioned on rvc = cond. mpdf::evalcond mpdf::samplecond 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 RV RV mpdf::rv rv modeled random variable mpdf::_rv mprod::mprod mlnorm< sq_T >::samplecond mpdf::samplecond mlnorm< sq_T >::set_parameters mgamma::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 mgamma::set_parameters Normal distributed linear function with linear function of mean value;. Mean value $mu=A*rvc$. mpdf mlnorm< sq_T > mEF epdf rv RV mpdf rv rvc ep mlnorm< sq_T > mEF mlnorm_epdf mlnorm_mu mlnorm_rv mlnorm_rvc mlnormA mlnormcondition mlnormcondition mlnormep mlnormepdf mlnormevalcond mlnormmEF mlnormmlnorm mlnormmpdf mlnormrv mlnormrvc mlnormsamplecond mlnormsamplecond mlnormsamplecond mlnormsamplecond mlnormset_parameters mlnorm~mpdf