#include <arx.h>

Regression of the following kind:
where unknown parameters rv are
, regression vector
is a known function of past outputs and exogeneous variables
. Distrubances
are supposed to be normally distributed:
See Theory of ARX model estimation for mathematical treatment.
The easiest way how to use the class is:
#include <estim/arx.h> using namespace bdm; // estimation of AR(0) model int main() { //prior mat V0 = 0.00001*eye(2); V0(0,0)= 0.1; // ARX Ar; Ar.set_statistics(1, V0); //nu is default (set to have finite moments) // forgetting is default: 1.0 mat Data = concat_vertical( randn(1,100), ones(1,100) ); Ar.bayesB( Data); cout << "Expected value of Theta is: " << Ar.posterior().mean() <<endl; }
Public Member Functions | |
| void | set_statistics (const BMEF *BM0) |
| Set sufficient statistics. | |
| BMEF * | _copy_ (bool changerv=false) |
| Flatten the posterior as if to keep nu0 data. | |
Constructors | |
| ARX (const double frg0=1.0) | |
| ARX (const ARX &A0) | |
| ARX * | _copy_ () |
| void | set_parameters (double frg0) |
| void | set_statistics (int dimx0, const ldmat V0, double nu0=-1.0) |
Mathematical operations | |
| void | bayes (const vec &dt, const double w) |
Weighted Bayes . | |
| void | bayes (const vec &dt) |
| Incremental Bayes rule. | |
| double | logpred (const vec &dt) const |
| void | flatten (const BMEF *B) |
| Flatten the posterior according to the given BMEF (of the same type!). | |
| enorm< ldmat > * | epredictor (const vec &rgr) const |
| Conditioned version of the predictor. | |
| enorm< ldmat > * | epredictor () const |
| Predictor for empty regressor. | |
| mlnorm< ldmat > * | predictor () const |
| conditional version of the predictor | |
| mlstudent * | predictor_student () const |
| ivec | structure_est (egiw Eg0) |
| Brute force structure estimation. | |
Access attributes | |
| const egiw * | _e () const |
| const egiw & | posterior () const |
Connection | |
| void | set_drv (const RV &drv0) |
| RV & | get_yrv () |
Mathematical operations | |
| virtual void | bayesB (const mat &Dt) |
| Batch Bayes rule (columns of Dt are observations). | |
| vec | logpred_m (const mat &dt) const |
| Matrix version of logpred. | |
Access to attributes | |
| const RV & | _drv () const |
| void | set_rv (const RV &rv) |
| double | _ll () const |
| void | set_evalll (bool evl0) |
Protected Attributes | |
| int | dimx |
| size of output variable (needed in regressors) | |
| RV | _yrv |
| egiw | est |
Posterior estimate of in the form of Normal-inverse Wishart density. | |
| ldmat & | V |
| cached value of est.V | |
| double & | nu |
| cached value of est.nu | |
| double | frg |
| forgetting factor | |
| double | last_lognc |
cached value of lognc() in the previous step (used in evaluation of ll ) | |
| RV | drv |
| Random variable of the data (optional). | |
| double | ll |
| Logarithm of marginalized data likelihood. | |
| bool | 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 computational time. | |
| ARX * bdm::ARX::_copy_ | ( | ) | [virtual] |
| void bdm::ARX::bayes | ( | const vec & | dt | ) | [inline, virtual] |
| double bdm::ARX::logpred | ( | const vec & | dt | ) | const [virtual] |
Evaluates predictive log-likelihood of the given data record I.e. marginal likelihood of the data with the posterior integrated out.
Reimplemented from bdm::BM.
References bdm::egiw::_nu(), bdm::egiw::_V(), est, bdm::BM::evalll, bdm::BMEF::frg, bdm::BMEF::last_lognc, bdm::egiw::lognc(), nu, ldmat::opupdt(), bdm::egiw::pow(), and V.
conditional version of the predictor
<----------- TODO
Reimplemented from bdm::BM.
References bdm::epdf::dimension(), est, bdm::egiw::mean_mat(), ldmat::rows(), bdm::mlnorm< sq_T >::set_parameters(), and V.
| ivec bdm::ARX::structure_est | ( | egiw | Eg0 | ) |
Brute force structure estimation.
References bdm::epdf::dimension(), est, and bdm::egiw::lognc().
RV bdm::ARX::_yrv [protected] |
description of modelled data
in the likelihood function Do NOT access directly, only via get_yrv().
1.5.6