bdm::Kalman< sq_T > Class Template Reference

Kalman filter with covariance matrices in square root form. More...

#include <libKF.h>

List of all members.

Public Member Functions
	Kalman ()
	Default constructor.
	Kalman (const Kalman< sq_T > &K0)
	Copy constructor.
void	set_parameters (const mat &A0, const mat &B0, const mat &C0, const mat &D0, const sq_T &Q0, const sq_T &R0)
	Set parameters with check of relevance.
void	set_est (const vec &mu0, const sq_T &P0)
	Set estimate values, used e.g. in initialization.
void	bayes (const vec &dt)
	Here dt = [yt;ut] of appropriate dimensions.
const epdf &	posterior () const
	access function
const enorm< sq_T > *	_e () const
mat &	__K ()
	access function
vec	_dP ()
	access function
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
RV	rvy
	Indetifier of output rv.
RV	rvu
	Indetifier of exogeneous rv.
int	dimx
	cache of rv.count()
int	dimy
	cache of rvy.count()
int	dimu
	cache of rvu.count()
mat	A
	Matrix A.
mat	B
	Matrix B.
mat	C
	Matrix C.
mat	D
	Matrix D.
sq_T	Q
	Matrix Q in square-root form.
sq_T	R
	Matrix R in square-root form.
enorm< sq_T >	est
	posterior density on $x_t$
enorm< sq_T >	fy
	preditive density on $y_t$
mat	_K
	placeholder for Kalman gain
vec &	_yp
	cache of fy.mu
sq_T &	_Ry
	cache of fy.R
vec &	_mu
	cache of est.mu
sq_T &	_P
	cache of est.R
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
void	set_options (const string &opt)
	Set boolean options from a string.
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.

---

Detailed Description

template<class sq_T>
class bdm::Kalman< sq_T >

Kalman filter with covariance matrices in square root form.

Parameter evolution model:

Observation model:

Where $e_t$ and $w_t$ are independent vectors Normal(0,1)-distributed disturbances.

---

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().

---

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

• libKF.h