MPF< BM_T > Class Template Reference

Marginalized Particle filter. More...

#include <libPF.h>

Inheritance diagram for MPF< BM_T >:

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Collaboration diagram for MPF< BM_T >:

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List of all members.

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.
epdf &	_epdf ()
	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!
void	bayes (mat Dt)
	Batch Bayes rule (columns of Dt are observations).
const RV &	_rv () const
	access function
double	_ll () const
	access function
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 time.
Classes
class	mpfepdf
	internal class for MPDF providing composition of eEmp with external components

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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).

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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().

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The documentation for this class was generated from the following file:

• work/git/mixpp/bdm/estim/libPF.h