Kalman< sq_T > Class Template Reference

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

#include <libKF.h>

Inheritance diagram for Kalman< sq_T >:

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Collaboration diagram for Kalman< sq_T >:

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List of all members.

Public Member Functions
	Kalman (int dimx, int dimu, int dimy)
	Default constructor.
	Kalman (mat A0, mat B0, mat C0, mat D0, sq_T R0, sq_T Q0, sq_T P0, vec mu0)
	Full constructor.
void	bayes (const vec &dt, bool evalll=true)
	Here dt = [yt;ut] of appropriate dimensions.
virtual void	bayes (const vec &dt)=0
	Incremental Bayes rule.
void	bayes (mat Dt)
	Batch Bayes rule (columns of Dt are observations).
epdf *	_epdf ()
	Returns a pointer to the epdf representing posterior density on parameters. Use with care!
Public Attributes
vec	mu
	Mean value of the posterior density.
sq_T	P
	Mean value of the posterior density.
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 time.
Protected Attributes
int	dimx
int	dimy
int	dimu
mat	A
mat	B
mat	C
mat	D
sq_T	R
sq_T	Q
mat	_K
vec	_yp
sq_T	_Ry
sq_T	_iRy
Friends
std::ostream &	operator<< (std::ostream &os, const KalmanFull &kf)

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Detailed Description

template<class sq_T>
class Kalman< sq_T >

Kalman filter with covariance matrices in square root form.

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Member Function Documentation

virtual void BM::bayes

(

const vec &

dt

)

[pure virtual, inherited]

Incremental Bayes rule.

Parameters:

dt

vector of input data

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

• work/mixpp/bdm/estim/libKF.h

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