MPF< BM_T > Class Template Reference
Marginalized Particle filter. More...
#include <libPF.h>
Inheritance diagram for MPF< BM_T >:
Collaboration diagram for MPF< BM_T >:
Public Member Functions | |
| MPF (const RV &rvlin, const RV &rvpf, mpdf &par0, mpdf &obs0, int n, const BM_T &BMcond0) | |
| Default constructor. | |
| void | bayes (const vec &dt) |
| Incremental Bayes rule. | |
| const epdf & | _epdf () const |
| Returns a reference to the epdf representing posterior density on parameters. | |
| const epdf * | _e () const |
| Returns a pointer to the epdf representing posterior density on parameters. Use with care! | |
| 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! | |
| 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 * | predictor (const RV &rv) const |
| Constructs a predictive density (marginal density on data). | |
| const RV & | _rv () const |
| access function | |
| double | _ll () const |
| access function | |
| void | set_evalll (bool evl0) |
| access function | |
| virtual BM * | _copy_ (bool changerv=false) |
Public Attributes | |
| double | SSAT |
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 | rv |
| Random variable of the posterior. | |
| 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 | |
Detailed Description
template<class BM_T>
class MPF< BM_T >
Marginalized Particle filter.
Trivial version: proposal = parameter evolution, observation model is not used. (it is assumed to be part of BM).
Member Function Documentation
template<class BM_T>
| void MPF< BM_T >::bayes | ( | const vec & | dt | ) | [inline, virtual] |
Incremental Bayes rule.
- Parameters:
-
dt vector of input data
Reimplemented from PF.
References PF::_samples, PF::_w, PF::est, PF::n, PF::par, eEmp::resample(), and mpdf::samplecond().
| virtual double 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 ARX, MixEF, and multiBM.
Referenced by BM::logpred_m().
| virtual BM* BM::_copy_ | ( | bool | changerv = false |
) | [inline, virtual, inherited] |
The documentation for this class was generated from the following file:
- work/git/mixpp/bdm/estim/libPF.h
