bdm::EKFful_unQR Class Reference

Extended Kalman filter with unknown Q and R. More...

#include <ekf_template.h>

List of all members.

Public Member Functions
void	condition (const vec &QR0)
	Substitute val for rvc.
void	set_parameters (const shared_ptr< diffbifn > &pfxu, const shared_ptr< diffbifn > &phxu, const mat Q0, const mat R0)
	Set nonlinear functions for mean values and covariance matrices.
void	bayes (const vec &dt)
	Here dt = [yt;ut] of appropriate dimensions.
void	set_statistics (vec mu0, mat P0)
	set estimates
const epdf &	posterior () const
	dummy!
const mat	_R ()
virtual string	to_string ()
	This method returns a basic info about the current instance.
virtual void	from_setting (const Setting &set)
	This method arrange instance properties according the data stored in the Setting structure.
virtual void	to_setting (Setting &set) const
	This method save all the instance properties into the Setting structure.
virtual void	validate ()
	This method TODO.
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 conditional density of 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)
Public Attributes
vec	mu
	Mean value of the posterior density.
mat	P
	Variance of the posterior density.
bool	evalll
double	ll
Protected Attributes
shared_ptr< diffbifn >	pfxu
	Internal Model f(x,u).
shared_ptr< diffbifn >	phxu
	Observation Model h(x,u).
enorm< fsqmat >	E
int	dimx
int	dimy
int	dimu
mat	A
mat	B
mat	C
mat	D
mat	R
mat	Q
mat	_Pp
mat	_Ry
mat	_iRy
mat	_K
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.
double	ll
	Logarithm of marginalized data likelihood.
RV	drv
	Random variable of the data (optional).
Friends
std::ostream &	operator<< (std::ostream &os, const KalmanFull &kf)
	print elements of KF
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
RV	rvc
	Name of extension variable.
Logging of results
virtual void	set_options (const string &opt)
	Set boolean options from a string, recognized are: "logbounds,logll".
virtual void	log_add (logger &L, const string &name="")
	Add all logged variables to a logger.
virtual void	logit (logger &L)
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.

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Detailed Description

Extended Kalman filter with unknown Q and R.

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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.

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:

• ekf_template.h