#include <libKF.h>


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. | |
| epdf & | _epdf () |
| access function | |
| void | bayes (mat Dt) |
| Batch Bayes rule (columns of Dt are observations). | |
| const RV & | _rv () const |
| access function | |
| double | _ll () 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. | |
| 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 | |
| sq_T * | _iRy |
| cache of fy.iR | |
| vec * | _mu |
| cache of est.mu | |
| sq_T * | _P |
| cache of est.R | |
| sq_T * | _iP |
| cache of est.iR | |
| 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 time. | |
Parameter evolution model:
Observation model:
Where $e_t$ and $w_t$ are independent vectors Normal(0,1)-distributed disturbances.
1.5.3