bdm::MPF< BM_T > Class Template Reference

Marginalized Particle filter. More...

#include <libPF.h>

List of all members.

Classes
class	mpfepdf
	internal class for MPDF providing composition of eEmp with external components
Public Member Functions
	MPF ()
	Default constructor.
void	set_parameters (mpdf *par0, mpdf *obs0, int n0, RESAMPLING_METHOD rm=SYSTEMATIC)
void	set_statistics (epdf *epdf0, const BM_T *BMcond0)
void	bayes (const vec &dt)
	Incremental Bayes rule.
const epdf &	posterior () const
const epdf *	_e () const
void	set_options (const string &opt)
	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_statistics (const vec w0, epdf *epdf0)
Constructors
virtual BM *	_copy_ () const
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.
RESAMPLING_METHOD	resmethod
	which resampling method will be used
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.
Options
bool	opt_L_smp
	Log all samples.
bool	opt_L_wei
	Log all samples.
Extension to conditional BM
This extension is useful e.g. in Marginalized Particle Filter (bdm::MPF). Alternatively, it can be used for automated connection to DS when the condition is observed
const RV &	_rvc () const
	access function
virtual void	condition (const vec &val)
	Substitute val for rvc.
RV	rvc
	Name of extension variable.
Logging of results
void	log_add (logger *L, const string &name="")
	Add all logged variables to a logger.
void	logit (logger *L)
ivec	LIDs
	IDs of storages in loggers.
bool	opt_L_bounds
	Option for logging bounds.

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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).

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Member Function Documentation

virtual BM* bdm::BM::_copy_

(

)

const [inline, virtual, inherited]

Copy function required in vectors, Arrays of BM etc. Have to be DELETED manually! Prototype:

 BM* _copy_() const {return new BM(*this);} 

Reimplemented in bdm::ARX, bdm::KalmanCh, bdm::EKF< sq_T >, bdm::EKFCh, and bdm::BMEF.

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::par, bdm::eEmp::resample(), bdm::PF::resmethod, and bdm::mpdf::samplecond().

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().

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

• libPF.h