bdm::MPF Class Reference

Marginalized Particle filter. More...

Detailed Description

Marginalized Particle filter.

A composition of particle filter with exact (or almost exact) bayesian models (BMs). The Bayesian models provide marginalized predictive density. Internaly this is achieved by virtual class MPFmpdf.

#include <particles.h>

List of all members.

Classes
class	mpfepdf
	internal class for MPDF providing composition of eEmp with external components More...
Public Member Functions
	MPF ()
	Default constructor.
void	set_parameters (shared_ptr< mpdf > par0, shared_ptr< mpdf > obs0, int n0, RESAMPLING_METHOD rm=SYSTEMATIC)
	set all parameters at once
void	set_BM (const BM &BMcond0)
	set a prototype of BM, copy it to as many times as there is particles in pf
void	bayes (const vec &dt)
	Incremental Bayes rule.
const epdf &	posterior () const
	return posterior density
void	set_options (const string &opt)
const BM *	_BM (int i)
	Access function.
void	from_setting (const Setting &set)
void	validate ()
	This method TODO.
virtual string	to_string ()
	This method returns a basic info about the current instance.
virtual void	to_setting (Setting &set) const
	This method save all the instance properties into the Setting structure.
Constructors

virtual BM *	_copy_ () const
	Copy function required in vectors, Arrays of BM etc. Have to be DELETED manually! Prototype:.
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 $f(d_{t+1} \|d_{t}, \ldots d_{0})$ .
virtual mpdf *	predictor () const
	Constructs conditional density of 1-step ahead predictor $f(d_{t+1} \|d_{t+h-1}, \ldots d_{t})$ .
Access to attributes
access function
const RV &	_drv () const
void	set_drv (const RV &rv)
	access function
void	set_rv (const RV &rv)
	access to rv of the posterior
double	_ll () const
	return internal log-likelihood of the last data vector
void	set_evalll (bool evl0)
	switch evaluation of log-likelihood on/off
Protected Attributes
shared_ptr< PF >	pf
	particle filter on non-linear variable
Array< BM * >	BMs
	Array of Bayesian models.
mpfepdf	jest
	Density joining PF.est with conditional parts.
bool	opt_L_mea
	Log means of BMs.
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.
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

virtual void	log_add (logger &L, const string &name="")
	Add all logged variables to a logger.
virtual void	logit (logger &L)
	Save results to the given logger, details of what is stored is configured by `LIDs` and `options`.
ivec	LIDs
	IDs of storages in loggers 4:[1=mean,2=lb,3=ub,4=ll].
ivec	LFlags
	Flags for logging - same size as LIDs, each entry correspond to the same in LIDs.

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::ARXfrg, bdm::KalmanFull, bdm::KalmanCh, bdm::EKFCh, and bdm::BMEF.

Referenced by set_BM().

void bdm::MPF::bayes ( const vec & dt ) [virtual]

Incremental Bayes rule.

Parameters:

dt

vector of input data

Implements bdm::BM.

References BMs, bdm::BM::condition(), and pf.

void bdm::MPF::from_setting ( const Setting & set ) [inline, virtual]

configuration structure for basic PF

        BM              = BM_class;           // Bayesian filtr for analytical part of the model
        parameter_pdf   = mpdf_class;         // transitional pdf for non-parametric part of the model
        prior           = epdf_class;         // prior probability density
        --- optional ---
        n               = 10;                 // number of particles
        resmethod       = 'systematic', or 'multinomial', or 'stratified'
                                                                                  // resampling method

Reimplemented from bdm::BM.

References bdm::UI::get(), pf, set_BM(), bdm::BM::set_drv(), set_options(), and validate().

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.

References bdm_error.

Referenced by bdm::BM::logpred_m().

void bdm::MPF::set_options ( const string & opt ) [inline, virtual]

Extends options understood by BM::set_options by option