Bayesian methods for autoregressive models
We describe a variational Bayesian (VB) learning algorithm for parameter estimation and model order selection in antoregressive (AR) models. With uninformative priors on the precisions of the coefficient and noise distributions the VB framework is shown to be identical to the Bayesian evidence framework. The VB model order selection criterion is compared with the minimum description length (MDL) criterion on synthetic data and on EEG.