EKF Kalman< fsqmat > libKF.h class sq_T sq_T diffbifn * diffbifn* EKF< sq_T >::pfxu pfxu Internal Model f(x,u). diffbifn * diffbifn* EKF< sq_T >::phxu phxu Observation Model h(x,u). EKF< sq_T >::EKF (RV rvx, RV rvy, RV rvu) EKF RV rvx RV rvy RV rvu Default constructor. void void EKF< sq_T >::set_parameters (diffbifn *pfxu, diffbifn *phxu, const sq_T Q0, const sq_T R0) set_parameters diffbifn * pfxu diffbifn * phxu const sq_T Q0 const sq_T R0 Set nonlinear functions for mean values and covariance matrices. Kalman< fsqmat >::_mu Kalman< fsqmat >::A Kalman< fsqmat >::B Kalman< fsqmat >::C Kalman< fsqmat >::D diffbifn::dfdx_cond Kalman< fsqmat >::dimu Kalman< fsqmat >::Q Kalman< fsqmat >::R void void EKF< sq_T >::bayes (const vec &dt) bayes bayes const vec & dt Here dt = [yt;ut] of appropriate dimensions. Kalman< fsqmat >::_K Kalman< fsqmat >::_mu Kalman< fsqmat >::_P Kalman< fsqmat >::_Ry Kalman< fsqmat >::_yp Kalman< fsqmat >::A Kalman< fsqmat >::C diffbifn::dfdx_cond Kalman< fsqmat >::dimu Kalman< fsqmat >::dimy diffbifn::eval BM::evalll enorm< sq_T >::evalpdflog Kalman< fsqmat >::fy fsqmat::inv BM::ll fsqmat::mult_sym Kalman< fsqmat >::Q Kalman< fsqmat >::R fsqmat::to_mat void void Kalman< fsqmat >::set_parameters (const mat &A0, const mat &B0, const mat &C0, const mat &D0, const fsqmat &R0, const fsqmat &Q0) set_parameters const mat & A0 const mat & B0 const mat & C0 const mat & D0 const fsqmat & R0 const fsqmat & Q0 Set parameters with check of relevance. void void Kalman< fsqmat >::set_est (const vec &mu0, const fsqmat &P0) set_est const vec & mu0 const fsqmat & P0 Set estimate values, used e.g. in initialization. Kalman< sq_T >::C Kalman< sq_T >::dimy Kalman< sq_T >::est Kalman< sq_T >::fy void void BM::bayes (mat Dt) bayes mat Dt Batch Bayes rule (columns of Dt are observations). epdf & epdf& Kalman< fsqmat >::_epdf () _epdf _epdf access function Kalman< sq_T >::est mat & mat& Kalman< fsqmat >::__K () __K access function Kalman< sq_T >::_K vec vec Kalman< fsqmat >::_dP () _dP access function Kalman< sq_T >::_P const RV & const RV& BM::_rv () const _rv access function BM::rv double double BM::_ll () const _ll access function BM::ll RV RV Kalman< fsqmat >::rvy rvy Indetifier of output rv. RV RV Kalman< fsqmat >::rvu rvu Indetifier of exogeneous rv. int int Kalman< fsqmat >::dimx dimx cache of rv.count() int int Kalman< fsqmat >::dimy dimy cache of rvy.count() EKF< sq_T >::bayes int int Kalman< fsqmat >::dimu dimu cache of rvu.count() EKF< sq_T >::bayes EKF< sq_T >::set_parameters mat mat Kalman< fsqmat >::A A Matrix A. EKF< sq_T >::bayes EKF< sq_T >::set_parameters mat mat Kalman< fsqmat >::B B Matrix B. EKF< sq_T >::set_parameters mat mat Kalman< fsqmat >::C C Matrix C. EKF< sq_T >::bayes EKF< sq_T >::set_parameters mat mat Kalman< fsqmat >::D D Matrix D. EKF< sq_T >::set_parameters fsqmat fsqmat Kalman< fsqmat >::Q Q Matrix Q in square-root form. EKF< sq_T >::bayes EKF< sq_T >::set_parameters fsqmat fsqmat Kalman< fsqmat >::R R Matrix R in square-root form. EKF< sq_T >::bayes EKF< sq_T >::set_parameters enorm< fsqmat > enorm<fsqmat > Kalman< fsqmat >::est est posterior density on $x_t$ enorm< fsqmat > enorm<fsqmat > Kalman< fsqmat >::fy fy preditive density on $y_t$ EKF< sq_T >::bayes mat mat Kalman< fsqmat >::_K _K placeholder for Kalman gain EKF< sq_T >::bayes vec & vec& Kalman< fsqmat >::_yp _yp cache of fy.mu EKF< sq_T >::bayes fsqmat & fsqmat & Kalman< fsqmat >::_Ry _Ry cache of fy.R EKF< sq_T >::bayes vec & vec& Kalman< fsqmat >::_mu _mu cache of est.mu EKF< sq_T >::bayes EKF< sq_T >::set_parameters fsqmat & fsqmat & Kalman< fsqmat >::_P _P cache of est.R EKF< sq_T >::bayes 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 Extended Kalman Filter. An approximation of the exact Bayesian filter with Gaussian noices and non-linear evolutions of their mean. EKF< sq_T > Kalman< fsqmat > < fsqmat > BM Kalman< sq_T > EKF< sq_T > pfxu phxu Kalman< fsqmat > rvu rvy _P Q R _Ry est fy < fsqmat > enorm< sq_T > R sq_T epdf rv BM rv fsqmat enorm< fsqmat > R < fsqmat > RV sqmat diffbifn rvu rvx Kalman< sq_T > _P Q R _Ry rvu rvy fnc eEF EKF__K EKF_dP EKF_epdf EKF_K EKF_ll EKF_mu EKF_P EKF_rv EKF_Ry EKF_yp EKFA EKFB EKFbayes EKFbayes EKFBM EKFC EKFD EKFdimu EKFdimx EKFdimy EKFEKF EKFest EKFevalll EKFfy EKFKalman EKFKalman EKFll EKFpfxu EKFphxu EKFQ EKFR EKFrv EKFrvu EKFrvy EKFset_est EKFset_parameters EKFset_parameters EKF~BM