EKF_unQful EKFfull BMcond EKF_unQful::EKF_unQful (RV rx, RV ry, RV ru, RV rQ) EKF_unQful RV rx RV ry RV ru RV rQ Default constructor. void void EKF_unQful::condition (const vec &Q0) condition const vec & Q0 void void EKF_unQful::bayes (const vec dt) bayes const vec dt void void EKFfull::set_parameters (diffbifn *pfxu, diffbifn *phxu, const mat Q0, const mat R0) set_parameters diffbifn * pfxu diffbifn * phxu const mat Q0 const mat R0 Set nonlinear functions for mean values and covariance matrices. diffbifn::_dimu fnc::_dimy RV::count diffbifn::dfdx_cond KalmanFull::mu BM::rv main void void EKFfull::bayes (const vec &dt) bayes bayes const vec & dt Here dt = [yt;ut] of appropriate dimensions. diffbifn::dfdx_cond diffbifn::eval BM::evalll BM::ll KalmanFull::mu KalmanFull::P enorm< sq_T >::set_mu main void void BM::bayes (mat Dt) bayes mat Dt Batch Bayes rule (columns of Dt are observations). void void EKFfull::set_est (vec mu0, mat P0) set_est vec mu0 mat P0 set estimates KalmanFull::mu KalmanFull::P main epdf & epdf& EKFfull::_epdf () _epdf _epdf dummy! main const RV & const RV& BM::_rv () const _rv access function BM::rv double double BM::_ll () const _ll access function BM::ll const RV & const RV& BMcond::_rvc () const _rvc access function BMcond::rvc int int KalmanFull::dimx dimx int int KalmanFull::dimy dimy int int KalmanFull::dimu dimu mat mat KalmanFull::A A mat mat KalmanFull::B B mat mat KalmanFull::C C mat mat KalmanFull::D D mat mat KalmanFull::R R mat mat KalmanFull::Q Q mat mat KalmanFull::_Pp _Pp mat mat KalmanFull::_Ry _Ry mat mat KalmanFull::_iRy _iRy mat mat KalmanFull::_K _K 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 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 RV RV BM::rv rv Random variable of the posterior. BM::_rv MPF< BM_T >::MPF EKFfull::set_parameters ARX::structure_est RV RV BMcond::rvc rvc Identificator of the conditioning variable. BMcond::_rvc KFcondR::condition KFcondQR::condition vec vec KalmanFull::mu mu Mean value of the posterior density. EKFfull::bayes KalmanFull::bayes KalmanFull::KalmanFull EKFfull::set_est EKFfull::set_parameters mat mat KalmanFull::P P Variance of the posterior density. EKFfull::bayes KalmanFull::bayes KalmanFull::KalmanFull EKFfull::set_est bool bool KalmanFull::evalll evalll double double KalmanFull::ll ll friend std::ostream & std::ostream& operator<< (std::ostream &os, const KalmanFull &kf) operator<< std::ostream & os const KalmanFull & kf print elements of KF BM EKF_unQful EKFfull BMcond KalmanFull enorm< sq_T > R sq_T epdf rv BM rv fsqmat EKF_unQful enorm< fsqmat > R < fsqmat > RV sqmat diffbifn rvu rvx fnc eEF EKFfull pfxu phxu E BMcond rvc KalmanFull EKF_unQful_epdf EKF_unQful_iRy EKF_unQful_K EKF_unQful_ll EKF_unQful_Pp EKF_unQful_rv EKF_unQful_rvc EKF_unQful_Ry EKF_unQfulA EKF_unQfulB EKF_unQfulbayes EKF_unQfulbayes EKF_unQfulbayes EKF_unQfulBM EKF_unQfulBMcond EKF_unQfulC EKF_unQfulcondition EKF_unQfulD EKF_unQfuldimu EKF_unQfuldimx EKF_unQfuldimy EKF_unQfulEKF_unQful EKF_unQfulEKFfull EKF_unQfulevalll EKF_unQfulevalll EKF_unQfulKalmanFull EKF_unQfulKalmanFull EKF_unQfulll EKF_unQfulll EKF_unQfulmu EKF_unQfuloperator<< EKF_unQfulP EKF_unQfulQ EKF_unQfulR EKF_unQfulrv EKF_unQfulrvc EKF_unQfulset_est EKF_unQfulset_parameters EKF_unQful~BM EKF_unQful~BMcond