#include <arx.h>


Public Member Functions | |
| ARX (RV &rv, mat &V0, double &nu0, double frg0=1.0) | |
| Full constructor. | |
| void | bayes (const vec &dt) |
Here . | |
| epdf & | _epdf () |
| Returns a pointer to the epdf representing posterior density on parameters. Use with care! | |
| ivec | structure_est (egiw Eg0) |
| Brute force structure estimation. | |
| void | bayes (mat Dt) |
| Batch Bayes rule (columns of Dt are observations). | |
| const RV & | _rv () const |
| access function | |
| double | _ll () const |
| access function | |
Protected Attributes | |
| 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 | |
| RV | rv |
| Random variable of the posterior. | |
| 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 time. | |
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:
Extension for time-variant parameters
may be achived using exponential forgetting (Kulhavy and Zarrop, 1993). In such a case, the forgetting factor frg
should be given in the constructor. Time-invariant parameters are estimated for frg = 1.
| ivec ARX::structure_est | ( | egiw | Eg0 | ) |
Brute force structure estimation.
References RV::count(), est, egiw::lognc(), and BM::rv.
1.5.5