bdm::MPF< BM_T > Class Template Reference
#include <libPF.h>
Inheritance diagram for bdm::MPF< BM_T >:
Detailed Description
template<class BM_T>
class bdm::MPF< BM_T >
Marginalized Particle filter.
Trivial version: proposal = parameter evolution, observation model is not used. (it is assumed to be part of BM).
Public Member Functions | |
| MPF (mpdf *par0, mpdf *obs0, int n, const BM_T &BMcond0) | |
| Default constructor. | |
| void | bayes (const vec &dt) |
| Incremental Bayes rule. | |
| const epdf & | posterior () const |
| const epdf * | _e () const |
| void | set_est (const epdf &epdf0) |
Set postrior of rvc to samples from epdf0. Statistics of Bms are not re-computed! Use only for initialization! | |
| BM * | _BM (int i) |
| Access function. | |
| vec * | __w () |
| access function | |
Constructors | |
| 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) |
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. | |
Classes | |
| class | mpfepdf |
| internal class for MPDF providing composition of eEmp with external components | |
Member Function Documentation
template<class BM_T>
| void bdm::MPF< BM_T >::bayes | ( | const vec & | dt | ) | [inline, virtual] |
Incremental Bayes rule.
- Parameters:
-
dt vector of input data
Reimplemented from bdm::PF.
References bdm::mpdf::_e(), bdm::PF::_samples, bdm::PF::_w, bdm::PF::est, bdm::epdf::evallog(), bdm::PF::n, bdm::PF::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().
The documentation for this class was generated from the following file:
