bdm::PF Class Reference

#include <libPF.h>

Inheritance diagram for bdm::PF:

List of all members.

---

Detailed Description

Trivial particle filter with proposal density equal to parameter evolution model.

Posterior density is represented by a weighted empirical density (eEmp ).

Public Member Functions
void	set_est (const epdf &epdf0)
void	bayes (const vec &dt)
	Incremental Bayes rule.
vec *	__w ()
	access function
Constructors
	PF ()
	PF (mpdf *par0, mpdf *obs0, epdf *epdf0, int n0)
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)
virtual const epdf &	posterior () const =0
virtual const epdf *	_e () const =0
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.

---

Member Function Documentation

void bdm::PF::set_est

(

const epdf &

epdf0

)

Set posterior density by sampling from epdf0

Reimplemented in bdm::MPF< BM_T >.

References _samples, n, and bdm::epdf::sample().

Referenced by bdm::MPF< BM_T >::set_est().

void bdm::PF::bayes

(

const vec &

dt

)

[virtual]

Incremental Bayes rule.

Parameters:

dt

vector of input data

Implements bdm::BM.

Reimplemented in bdm::MPF< BM_T >.

References bdm::mpdf::_e(), _samples, _w, est, bdm::epdf::evallog(), bdm::mpdf::evallogcond(), n, obs, par, bdm::eEmp::resample(), and bdm::mpdf::samplecond().

virtual BM* bdm::BM::_copy_

(

)

[inline, virtual, inherited]

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

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

Reimplemented in bdm::ARX.

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 files:

• libPF.h
• libPF.cpp