#include <arx.h>
Public Member Functions | |
| void | set_statistics (const BMEF *BM0) |
| Set sufficient statistics. | |
| void | from_setting (const Setting &root) |
| virtual string | to_string () |
| This method returns a basic info about the current instance. | |
| virtual void | to_setting (Setting &root) const |
| This method save all the instance properties into the Setting structure. | |
| virtual void | validate () |
| This method TODO. | |
Constructors | |
| ARX (const double frg0=1.0) | |
| ARX (const ARX &A0) | |
| ARX * | _copy_ () const |
| Flatten the posterior as if to keep nu0 data. | |
| 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. | |
Extension to conditional BM | |
| This extension is useful e.g. in Marginalized Particle Filter (bdm::MPF). Alternatively, it can be used for automated connection to DS when the condition is observed | |
| const RV & | _rvc () const |
| access function | |
| virtual void | condition (const vec &val) |
Substitute val for rvc. | |
| RV | rvc |
| Name of extension variable. | |
Logging of results | |
| virtual void | set_options (const string &opt) |
| Set boolean options from a string recognized are: "logbounds,logll". | |
| virtual void | log_add (logger &L, const string &name="") |
| Add all logged variables to a logger. | |
| virtual void | logit (logger &L) |
| ivec | LIDs |
| IDs of storages in loggers 4:[1=mean,2=lb,3=ub,4=ll]. | |
| ivec | LFlags |
| Flags for logging - same size as LIDs, each entry correspond to the same in LIDs. | |
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; }
| void bdm::ARX::bayes | ( | const vec & | dt | ) | [inline, virtual] |
| void bdm::ARX::from_setting | ( | const Setting & | root | ) | [virtual] |
The ARX is constructed from a structure with fields:
estimator = {
type = "ARX";
y = {type="rv", ...} // description of output variables
rgr = {type="rv", ...} // description of regressor variables
constant = true; // boolean switch if the constant term is modelled or not
//optional fields
dV0 = [1e-3, 1e-5, 1e-5, 1e-5];
// default: 1e-3 for y, 1e-5 for rgr
nu0 = 6; // default: rgrlen + 2
frg = 1.0; // forgetting, default frg=1.0
};
The estimator will assign names of the posterior in the form ["theta_i" and "r_i"]
Reimplemented from bdm::bdmroot.
References bdm::RV::_dsize(), bdm::BMEF::frg, and bdm::UI::get().
| 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().
1.5.8