Variational Bayes for non-Gaussian autoregressive models
We describe a variational Bayesian (VB) learning algorithm for Non-Gaussian Autoregressive (AR) models. The noise is modelled as a mixture of Gaussians rather than the usual single Gaussian. This allows different data points to be associated with different noise levels and effectively provides a robust estimation of AR coefficients. The VB framework is used to prevent overfitting and provides model order selection criteria both for AR order and noise model order. The algorithm is applied to synthetic data and to EEG.