Kalman BM libKF.h class sq_T sq_T RV RV Kalman< sq_T >::rvy rvy Indetifier of output rv. RV RV Kalman< sq_T >::rvu rvu Indetifier of exogeneous rv. int int Kalman< sq_T >::dimx dimx cache of rv.count() Kalman< sq_T >::set_parameters int int Kalman< sq_T >::dimy dimy cache of rvy.count() Kalman< sq_T >::bayes Kalman< fsqmat >::set_est Kalman< sq_T >::set_parameters int int Kalman< sq_T >::dimu dimu cache of rvu.count() Kalman< sq_T >::bayes Kalman< sq_T >::set_parameters mat mat Kalman< sq_T >::A A Matrix A. Kalman< sq_T >::bayes Kalman< sq_T >::Kalman Kalman< sq_T >::set_parameters mat mat Kalman< sq_T >::B B Matrix B. Kalman< sq_T >::bayes Kalman< sq_T >::Kalman Kalman< sq_T >::set_parameters mat mat Kalman< sq_T >::C C Matrix C. Kalman< sq_T >::bayes Kalman< sq_T >::Kalman Kalman< fsqmat >::set_est Kalman< sq_T >::set_parameters mat mat Kalman< sq_T >::D D Matrix D. Kalman< sq_T >::bayes Kalman< sq_T >::Kalman Kalman< sq_T >::set_parameters sq_T sq_T Kalman< sq_T >::Q Q Matrix Q in square-root form. Kalman< sq_T >::bayes Kalman< sq_T >::Kalman Kalman< sq_T >::set_parameters sq_T sq_T Kalman< sq_T >::R R Matrix R in square-root form. Kalman< sq_T >::bayes Kalman< sq_T >::Kalman Kalman< sq_T >::set_parameters enorm< sq_T > enorm<sq_T> Kalman< sq_T >::est est posterior density on $x_t$ Kalman< fsqmat >::_epdf Kalman< fsqmat >::set_est enorm< sq_T > enorm<sq_T> Kalman< sq_T >::fy fy preditive density on $y_t$ Kalman< sq_T >::bayes Kalman< fsqmat >::set_est mat mat Kalman< sq_T >::_K _K placeholder for Kalman gain Kalman< fsqmat >::__K Kalman< sq_T >::bayes vec & vec& Kalman< sq_T >::_yp _yp cache of fy.mu Kalman< sq_T >::bayes Kalman< sq_T >::Kalman sq_T & sq_T& Kalman< sq_T >::_Ry _Ry cache of fy.R Kalman< sq_T >::bayes Kalman< sq_T >::Kalman vec & vec& Kalman< sq_T >::_mu _mu cache of est.mu Kalman< sq_T >::bayes Kalman< sq_T >::Kalman sq_T & sq_T& Kalman< sq_T >::_P _P cache of est.R Kalman< fsqmat >::_dP Kalman< sq_T >::bayes Kalman< sq_T >::Kalman RV RV BM::rv rv Random variable of the posterior. BM::_rv MPF< BM_T >::MPF EKFfull::set_parameters ARX::structure_est double double BM::ll ll Logarithm of marginalized data likelihood. BM::_ll EKFfixed::bayes EKF< sq_T >::bayes Kalman< sq_T >::bayes EKFCh::bayes KalmanCh::bayes EKFfull::bayes ARX::bayes bool bool BM::evalll 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. EKFfixed::bayes EKF< sq_T >::bayes Kalman< sq_T >::bayes EKFCh::bayes KalmanCh::bayes EKFfull::bayes ARX::bayes Kalman< sq_T >::Kalman (RV rvx0, RV rvy0, RV rvu0) Kalman RV rvx0 RV rvy0 RV rvu0 Default constructor. Kalman< sq_T >::Kalman (const Kalman< sq_T > &K0) Kalman const Kalman< sq_T > & K0 Copy constructor. Kalman< sq_T >::_mu Kalman< sq_T >::_P Kalman< sq_T >::_Ry Kalman< sq_T >::_yp Kalman< sq_T >::A Kalman< sq_T >::B Kalman< sq_T >::C Kalman< sq_T >::D Kalman< sq_T >::Q Kalman< sq_T >::R Kalman< sq_T >::set_parameters void void Kalman< sq_T >::set_parameters (const mat &A0, const mat &B0, const mat &C0, const mat &D0, const sq_T &R0, const sq_T &Q0) set_parameters 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. Kalman< sq_T >::A Kalman< sq_T >::B Kalman< sq_T >::C Kalman< sq_T >::D Kalman< sq_T >::dimu Kalman< sq_T >::dimx Kalman< sq_T >::dimy Kalman< sq_T >::Q Kalman< sq_T >::R Kalman< sq_T >::Kalman void void Kalman< sq_T >::set_est (const vec &mu0, const sq_T &P0) set_est set_est const vec & mu0 const sq_T & P0 Set estimate values, used e.g. in initialization. void void Kalman< sq_T >::bayes (const vec &dt) bayes bayes bayes bayes bayes const vec & dt Here dt = [yt;ut] of appropriate dimensions. Kalman< sq_T >::_K Kalman< sq_T >::_mu Kalman< sq_T >::_P Kalman< sq_T >::_Ry Kalman< sq_T >::_yp Kalman< sq_T >::A Kalman< sq_T >::B Kalman< sq_T >::C Kalman< sq_T >::D Kalman< sq_T >::dimu Kalman< sq_T >::dimy BM::evalll Kalman< sq_T >::fy BM::ll Kalman< sq_T >::Q Kalman< sq_T >::R epdf & epdf& Kalman< sq_T >::_epdf () _epdf _epdf access function mat & mat& Kalman< sq_T >::__K () __K access function vec vec Kalman< sq_T >::_dP () _dP access function void void BM::bayes (mat Dt) bayes mat Dt Batch Bayes rule (columns of Dt are observations). const RV & const RV& BM::_rv () const _rv access function BM::rv double double BM::_ll () const _ll access function BM::ll 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. EKF< sq_T > Kalman< fsqmat > < fsqmat > EKFCh BM KFcondR EKF_unQ KFcondQR KalmanCh Kalman< ldmat > < ldmat > Kalman< chmat > < chmat > Kalman< sq_T > sq_T BM rv RV Kalman< sq_T > _P Q R _Ry rvu rvy Kalman__K Kalman_dP Kalman_epdf Kalman_K Kalman_ll Kalman_mu Kalman_P Kalman_rv Kalman_Ry Kalman_yp KalmanA KalmanB Kalmanbayes Kalmanbayes KalmanBM KalmanC KalmanD Kalmandimu Kalmandimx Kalmandimy Kalmanest Kalmanevalll Kalmanfy KalmanKalman KalmanKalman Kalmanll KalmanQ KalmanR Kalmanrv Kalmanrvu Kalmanrvy Kalmanset_est Kalmanset_parameters Kalman~BM