ARX BM arx.h egiw egiw ARX::est est Posterior estimate of $\theta,r$ in the form of Normal-inverse Wishart density. _epdf ARX bayes get_parameters set_parameters structure_est ldmat & ldmat& ARX::V V cached value of est.V bayes double & double& ARX::nu nu cached value of est.nu bayes double double ARX::frg frg forgetting factor bayes double double ARX::last_lognc last_lognc cached value of lognc() in the previous step (used in evaluation of ll ) ARX bayes set_parameters double double ARX::tll tll total likelihood _tll ARX bayes set_parameters RV RV BM::rv rv Random variable of the posterior. BM::_rv MPF< BM_T >::MPF EKFfull::set_parameters 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 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 bayes ARX::ARX (RV &rv, mat &V0, double &nu0, double frg0=1.0) ARX RV & rv mat & V0 double & nu0 double frg0 1.0 Full constructor. est last_lognc egiw::lognc tll void void ARX::set_parameters (mat &V0, double &nu0) set_parameters mat & V0 double & nu0 Set sufficient statistics. egiw::_nu egiw::_V est last_lognc egiw::lognc tll void void ARX::get_parameters (mat &V0, double &nu0) get_parameters mat & V0 double & nu0 Returns sufficient statistics. egiw::_nu egiw::_V est ldmat::to_mat void void ARX::bayes (const vec &dt) bayes bayes const vec & dt Here $dt = [y_t psi_t] $. est BM::evalll frg last_lognc BM::ll egiw::lognc nu ldmat::opupdt tll V epdf & epdf& ARX::_epdf () _epdf _epdf Returns a pointer to the epdf representing posterior density on parameters. Use with care! est ivec ivec ARX::structure_est (egiw Eg0) structure_est egiw Eg0 Brute force structure estimation. indeces of accepted regressors. RV::count est egiw::lognc BM::rv double double ARX::_tll () _tll access function tll 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 Linear Autoregressive model with Gaussian noise. Regression of the following kind: \[ y_t = \theta_1 \psi_1 + \theta_2 + \psi_2 +\ldots + \theta_n \psi_n + r e_t \] where unknown parameters rv are $[\theta r]$, regression vector $\psi=\psi(y_{1:t},u_{1:t})$ is a known function of past outputs and exogeneous variables $u_t$. Distrubances $e_t$ are supposed to be normally distributed: \[ e_t \sim \mathcal{N}(0,1). \]Extension for time-variant parameters $\theta_t,r_t$ may be achived using exponential forgetting (Kulhavy and Zarrop, 1993). In such a case, the forgetting factor frg $\in <0,1>$ should be given in the constructor. Time-invariant parameters are estimated for frg = 1. BM ARX ldmat epdf rv BM rv RV sqmat eEF ARX V est egiw V ARX_epdf ARX_ll ARX_rv ARX_tll ARXARX ARXbayes ARXbayes ARXBM ARXest ARXevalll ARXfrg ARXget_parameters ARXlast_lognc ARXll ARXnu ARXrv ARXset_parameters ARXstructure_est ARXtll ARXV ARX~BM