DocumentCode
1739132
Title
Bayesian methods for autoregressive models
Author
Penny, W.D. ; Roberts, S.J.
Author_Institution
Dept. of Eng. Sci., Oxford Univ., UK
Volume
1
fYear
2000
fDate
2000
Firstpage
125
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;
fLanguage
English
Publisher
ieee
Conference_Titel
Neural Networks for Signal Processing X, 2000. Proceedings of the 2000 IEEE Signal Processing Society Workshop
Conference_Location
Sydney, NSW
ISSN
1089-3555
Print_ISBN
0-7803-6278-0
Type
conf
DOI
10.1109/NNSP.2000.889369
Filename
889369
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