bdm::KFcondQR Class Reference

Kalman Filter with conditional diagonal matrices R and Q. More...

#include <libKF.h>

Inheritance diagram for bdm::KFcondQR:

[legend]

Collaboration diagram for bdm::KFcondQR:

[legend]

List of all members.

Public Member Functions
	KFcondQR (RV rvx, RV rvy, RV rvu, RV rvRQ)
	Default constructor.
void	condition (const vec &RQ)
	Substitute val for rvc.
void	set_parameters (const mat &A0, const mat &B0, const mat &C0, const mat &D0, const ldmat &R0, const ldmat &Q0)
	Set parameters with check of relevance.
void	set_est (const vec &mu0, const ldmat &P0)
	Set estimate values, used e.g. in initialization.
void	bayes (const vec &dt)
	Here dt = [yt;ut] of appropriate dimensions.
const epdf &	_epdf () const
	access function
const enorm< ldmat > *	_e () const
	Returns a pointer to the epdf representing posterior density on parameters. Use with care!
mat &	__K ()
	access function
vec	_dP ()
	access function
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 *	predictor (const RV &rv) const
	Constructs a predictive density (marginal density on data).
const RV &	_rv () const
	access function
double	_ll () const
	access function
void	set_evalll (bool evl0)
	access function
virtual BM *	_copy_ (bool changerv=false)
const RV &	_rvc () const
	access function
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.
ldmat	Q
	Matrix Q in square-root form.
ldmat	R
	Matrix R in square-root form.
enorm< ldmat >	est
	posterior density on $x_t$
enorm< ldmat >	fy
	preditive density on $y_t$
mat	_K
	placeholder for Kalman gain
vec &	_yp
	cache of fy.mu
ldmat &	_Ry
	cache of fy.R
vec &	_mu
	cache of est.mu
ldmat &	_P
	cache of est.R
RV	rv
	Random variable of the posterior.
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.
RV	rvc
	Identificator of the conditioning variable.

---

Detailed Description

Kalman Filter with conditional diagonal matrices R and Q.

---

Member Function Documentation

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().

virtual BM* bdm::BM::_copy_

(

bool

changerv = false

)

[inline, virtual, inherited]

Copy function required in vectors, Arrays of BM etc. Have to be DELETED manually! Prototype: BM* _copy_(){BM Tmp*=new Tmp(this*); return Tmp; }

Reimplemented in bdm::ARX, and bdm::BMEF.

---

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

• libKF.h
• libKF.cpp