Title :
Bayesian methods for autoregressive models
Author :
Penny, W.D. ; Roberts, S.J.
Author_Institution :
Dept. of Eng. Sci., Oxford Univ., UK
Abstract :
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
Keywords :
Bayes methods; autoregressive processes; parameter estimation; Bayesian methods; EEG; autoregressive models; minimum description length criterion; model order selection; noise distributions; parameter estimation; synthetic data; uninformative priors; variational Bayesian learning algorithm; Bayesian methods; Brain modeling; Context modeling; Electroencephalography; Gaussian noise; History; Maximum likelihood estimation; Parameter estimation; Principal component analysis; Stochastic processes;
Conference_Titel :
Neural Networks for Signal Processing X, 2000. Proceedings of the 2000 IEEE Signal Processing Society Workshop
Conference_Location :
Sydney, NSW
Print_ISBN :
0-7803-6278-0
DOI :
10.1109/NNSP.2000.889369
