bdm::KalmanCh Class Reference

Kalman filter in square root form. More...

#include <kalman.h>

List of all members.

Public Member Functions
BM *	_copy_ () const
	copy constructor
void	set_parameters (const mat &A0, const mat &B0, const mat &C0, const mat &D0, const chmat &Q0, const chmat &R0)
	Set parameters with check of relevance.
void	set_statistics (const vec &mu0, const chmat &P0)
void	bayes (const vec &dt)
	Here dt = [yt;ut] of appropriate dimensions.
void	set_est (const vec &mu0, const chmat &P0)
	Set estimate values, used e.g. in initialization.
const epdf &	posterior () const
	access function
const enorm< chmat > *	_e () const
mat &	__K ()
	access function
vec	_dP ()
	access function
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.
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
mat	preA
	pre array (triangular matrix)
mat	postA
	post array (triangular matrix)
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.
chmat	Q
	Matrix Q in square-root form.
chmat	R
	Matrix R in square-root form.
enorm< chmat >	est
	posterior density on $x_t$
enorm< chmat >	fy
	preditive density on $y_t$
mat	_K
	placeholder for Kalman gain
vec &	_yp
	cache of fy.mu
chmat &	_Ry
	cache of fy.R
vec &	_mu
	cache of est.mu
chmat &	_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
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

Kalman filter in square root form.

Trivial example:

#include "estim/kalman.h"
using namespace bdm;
        
// estimation of AR(0) model
int main() {
        //dimensions
        int dx=3, dy=3, du=1;
        // matrices
        mat A = eye(dx);
        mat B = zeros(dx,du);
        mat C = eye(dx);
        mat D = zeros(dy,du);
        mat Q = eye(dx);
        mat R = 0.1*eye(dy);
        //prior
        mat P0 = 100*eye(dx);
        vec mu0 = zeros(dx);
        // Estimator
        KalmanCh KF;
        KF.set_parameters(A,B,C,D,/*covariances*/ Q,R);
        KF.set_statistics(mu0,P0);
        // Estimation loop
        for (int i=0;i<100;i++){
                KF.bayes(randn(dx+du));
        }
        //print results
        cout << "Posterior estimate of x is: "  << endl;
        cout << "mean: "<< KF.posterior().mean()<< endl;
        cout << "variance: "<< KF.posterior().variance()<< endl;
}

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Member Function Documentation

void bdm::KalmanCh::bayes

(

const vec &

dt

)

[virtual]

Here dt = [yt;ut] of appropriate dimensions.

The following equality hold::

Thus this object evaluates only predictors! Not filtering densities.

Reimplemented from bdm::Kalman< chmat >.

Reimplemented in bdm::EKFCh.

References chmat::_Ch(), bdm::Kalman< chmat >::_K, bdm::Kalman< chmat >::_mu, bdm::Kalman< chmat >::_P, bdm::Kalman< chmat >::_Ry, bdm::Kalman< chmat >::_yp, bdm::Kalman< chmat >::A, bdm::Kalman< chmat >::B, bdm::Kalman< chmat >::C, bdm::Kalman< chmat >::D, bdm::Kalman< chmat >::dimu, bdm::Kalman< chmat >::dimx, bdm::Kalman< chmat >::dimy, bdm::BM::evalll, bdm::eEF::evallog(), bdm::Kalman< chmat >::fy, bdm::BM::ll, postA, and preA.

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 files:

• kalman.h
• kalman.cpp