#include <libPF.h>

Posterior density is represented by a weighted empirical density (eEmp ).
Public Member Functions | |
| void | set_est (const epdf &epdf0) |
| void | bayes (const vec &dt) |
| Incremental Bayes rule. | |
| vec * | __w () |
| access function | |
Constructors | |
| PF () | |
| PF (mpdf *par0, mpdf *obs0, epdf *epdf0, int n0) | |
| void | set_parameters (mpdf *par0, mpdf *obs0, int n0) |
| void | set_statistics (const vec w0, epdf *epdf0) |
Constructors | |
| virtual BM * | _copy_ () |
Mathematical operations | |
| virtual void | bayesB (const mat &Dt) |
| Batch Bayes rule (columns of Dt are observations). | |
| virtual double | logpred (const vec &dt) const |
| vec | logpred_m (const mat &dt) const |
| Matrix version of logpred. | |
| virtual epdf * | epredictor () const |
Constructs a predictive density . | |
| virtual mpdf * | predictor () const |
| Constructs a conditional density 1-step ahead predictor. | |
Access to attributes | |
| const RV & | _drv () const |
| void | set_drv (const RV &rv) |
| void | set_rv (const RV &rv) |
| double | _ll () const |
| void | set_evalll (bool evl0) |
| virtual const epdf & | posterior () const =0 |
| virtual const epdf * | _e () const =0 |
Protected Attributes | |
| int | n |
| number of particles; | |
| eEmp | est |
| posterior density | |
| vec & | _w |
pointer into eEmp | |
| Array< vec > & | _samples |
pointer into eEmp | |
| mpdf * | par |
| Parameter evolution model. | |
| mpdf * | obs |
| Observation model. | |
| RV | drv |
| Random variable of the data (optional). | |
| double | ll |
| Logarithm of marginalized data likelihood. | |
| bool | evalll |
If true, the filter will compute likelihood of the data record and store it in ll . Set to false if you want to save computational time. | |
| void bdm::PF::set_est | ( | const epdf & | epdf0 | ) |
Set posterior density by sampling from epdf0
Reimplemented in bdm::MPF< BM_T >.
References _samples, n, and bdm::epdf::sample().
Referenced by bdm::MPF< BM_T >::set_est().
| void bdm::PF::bayes | ( | const vec & | dt | ) | [virtual] |
Incremental Bayes rule.
| dt | vector of input data |
Implements bdm::BM.
Reimplemented in bdm::MPF< BM_T >.
References bdm::mpdf::_e(), _samples, _w, est, bdm::epdf::evallog(), bdm::mpdf::evallogcond(), n, obs, par, bdm::eEmp::resample(), and bdm::mpdf::samplecond().
| virtual BM* bdm::BM::_copy_ | ( | ) | [inline, virtual, inherited] |
| virtual double bdm::BM::logpred | ( | const vec & | dt | ) | const [inline, virtual, inherited] |
Evaluates predictive log-likelihood of the given data record I.e. marginal likelihood of the data with the posterior integrated out.
Reimplemented in bdm::ARX, bdm::MixEF, and bdm::multiBM.
Referenced by bdm::BM::logpred_m().
1.5.6