#include <arx.h>
Public Member Functions | |
void | set_statistics (const BMEF *BM0) |
Set sufficient statistics. | |
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 | |
void | set_options (const string &opt) |
Set boolean options from a string. | |
void | log_add (logger *L, const string &name="") |
Add all logged variables to a logger. | |
void | logit (logger *L) |
ivec | LIDs |
IDs of storages in loggers. | |
bool | opt_L_bounds |
Option for logging bounds. |
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] |
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()
.