# ARX Class Reference

Linear Autoregressive model with Gaussian noise. More...

`#include <arx.h>`

Inheritance diagram for ARX:

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Collaboration diagram for ARX:

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List of all members.

## Public Member Functions
| ARX (const RV &rv, const mat &V0, const double &nu0, const double frg0=1.0)
| Full constructor.
| ARX (const ARX &A0)
| Copy constructor.
ARX * | _copy_ (bool changerv=false)
| Auxiliary function.
void | set_parameters (const ldmat &V0, const double &nu0)
| Set sufficient statistics.
void | set_statistics (const BMEF *BM0)
| get statistics from another model
void | get_parameters (mat &V0, double &nu0)
| Returns sufficient statistics.
void | bayes (const vec &dt, const double w)
| Here .
void | bayes (const vec &dt)
| Incremental Bayes rule.
const epdf & | _epdf () const
| Returns a pointer to the epdf representing posterior density on parameters. Use with care!
double | logpred (const vec &dt) const
void | flatten (BMEF *B)
| Flatten the posterior.
ivec | structure_est (egiw Eg0)
| Brute force structure estimation.
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.
virtual epdf * | predictor (const RV &rv)
| Constructs a predictive density (marginal density on data).
const RV & | _rv () const
| access function
double | _ll () const
| access function
void | set_evalll (bool evl0)
| 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 (used in evaluation of `ll` )
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 computational time.

## Detailed Description

Linear Autoregressive model with Gaussian noise.

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.

## Member Function Documentation

void ARX::bayes | ( | const vec & | dt | ) | `[inline, virtual]`

Incremental Bayes rule.

Parameters:

  | dt | vector of input data

Reimplemented from BMEF.

References bayes().

double 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 BM.

References egiw::_nu(), egiw::_V(), est, BM::evalll, BMEF::frg, BMEF::last_lognc, egiw::lognc(), nu, ldmat::opupdt(), egiw::pow(), and V.

ivec ARX::structure_est | ( | egiw | Eg0 | ) |

Brute force structure estimation.

Returns:
  indeces of accepted regressors.

References RV::count(), est, egiw::lognc(), and BM::rv.

The documentation for this class was generated from the following files:

- work/git/mixpp/bdm/estim/arx.h
- work/git/mixpp/bdm/estim/arx.cpp