KFcondQR Kalman< ldmat > BMcond libKF.h KFcondQR::KFcondQR (RV rvx, RV rvy, RV rvu, RV rvRQ) KFcondQR RV rvx RV rvy RV rvu RV rvRQ Default constructor. void void KFcondQR::condition (const vec &RQ) condition condition const vec & val Substitute val for rvc. RV::count Kalman< ldmat >::dimx Kalman< ldmat >::Q Kalman< ldmat >::R BMcond::rvc ldmat::setD void void Kalman< ldmat >::set_parameters (const mat &A0, const mat &B0, const mat &C0, const mat &D0, const ldmat &R0, const ldmat &Q0) set_parameters const mat & A0 const mat & B0 const mat & C0 const mat & D0 const ldmat & R0 const ldmat & Q0 Set parameters with check of relevance. void void Kalman< ldmat >::set_est (const vec &mu0, const ldmat &P0) set_est const vec & mu0 const ldmat & P0 Set estimate values, used e.g. in initialization. void void Kalman< ldmat >::bayes (const vec &dt) bayes bayes const vec & dt Here dt = [yt;ut] of appropriate dimensions. void void BM::bayes (mat Dt) bayes mat Dt Batch Bayes rule (columns of Dt are observations). epdf & epdf& Kalman< ldmat >::_epdf () _epdf _epdf access function mat & mat& Kalman< ldmat >::__K () __K access function vec vec Kalman< ldmat >::_dP () _dP access function 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 RV RV Kalman< ldmat >::rvy rvy Indetifier of output rv. RV RV Kalman< ldmat >::rvu rvu Indetifier of exogeneous rv. int int Kalman< ldmat >::dimx dimx cache of rv.count() condition int int Kalman< ldmat >::dimy dimy cache of rvy.count() int int Kalman< ldmat >::dimu dimu cache of rvu.count() mat mat Kalman< ldmat >::A A Matrix A. mat mat Kalman< ldmat >::B B Matrix B. mat mat Kalman< ldmat >::C C Matrix C. mat mat Kalman< ldmat >::D D Matrix D. ldmat ldmat Kalman< ldmat >::Q Q Matrix Q in square-root form. condition ldmat ldmat Kalman< ldmat >::R R Matrix R in square-root form. KFcondR::condition condition enorm< ldmat > enorm<ldmat > Kalman< ldmat >::est est posterior density on $x_t$ enorm< ldmat > enorm<ldmat > Kalman< ldmat >::fy fy preditive density on $y_t$ mat mat Kalman< ldmat >::_K _K placeholder for Kalman gain vec & vec& Kalman< ldmat >::_yp _yp cache of fy.mu ldmat & ldmat & Kalman< ldmat >::_Ry _Ry cache of fy.R vec & vec& Kalman< ldmat >::_mu _mu cache of est.mu ldmat & ldmat & Kalman< ldmat >::_P _P cache of est.R 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 RV RV BMcond::rvc rvc Identificator of the conditioning variable. BMcond::_rvc KFcondR::condition condition Kalman Filter with conditional diagonal matrices R and Q. BM KFcondQR Kalman< ldmat > < ldmat > Kalman< sq_T > BMcond ldmat enorm< sq_T > R sq_T epdf rv BM rv RV KFcondQR sqmat Kalman< ldmat > _P Q R _Ry rvu rvy est fy < ldmat > enorm< ldmat > R < ldmat > Kalman< sq_T > _P Q R _Ry rvu rvy eEF BMcond rvc KFcondQR__K KFcondQR_dP KFcondQR_epdf KFcondQR_K KFcondQR_ll KFcondQR_mu KFcondQR_P KFcondQR_rv KFcondQR_rvc KFcondQR_Ry KFcondQR_yp KFcondQRA KFcondQRB KFcondQRbayes KFcondQRbayes KFcondQRBM KFcondQRBMcond KFcondQRC KFcondQRcondition KFcondQRD KFcondQRdimu KFcondQRdimx KFcondQRdimy KFcondQRest KFcondQRevalll KFcondQRfy KFcondQRKalman KFcondQRKalman KFcondQRKFcondQR KFcondQRll KFcondQRQ KFcondQRR KFcondQRrv KFcondQRrvc KFcondQRrvu KFcondQRrvy KFcondQRset_est KFcondQRset_parameters KFcondQR~BM KFcondQR~BMcond