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 >:

Inheritance graph
[legend]
Collaboration diagram for Kalman< sq_T >:

Collaboration graph
[legend]

List of all members.

Public Member Functions

 Kalman (RV rvx0, RV rvy0, RV rvu0)
 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 &R0, const sq_T &Q0)
 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_epdf () const
 access function
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
const RV_rv () const
 access function
double _ll () const
 access function
void set_evalll (bool evl0)
 access function
virtual BM_copy_ (bool changerv=false)

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 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.


Detailed Description

template<class sq_T>
class Kalman< sq_T >

Kalman filter with covariance matrices in square root form.

Parameter evolution model:

\[ x_t = A x_{t-1} + B u_t + Q^{1/2} e_t \]

Observation model:

\[ y_t = C x_{t-1} + C u_t + Q^{1/2} w_t. \]

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


Member Function Documentation

virtual double 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 ARX, MixEF, and multiBM.

virtual BM* 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 ARX.

Referenced by MixEF::MixEF().


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

Generated on Tue Sep 23 16:00:52 2008 for mixpp by  doxygen 1.5.6