Bayesian multivariate autoregressive models with structured priors

Author

Penny, W.D. ; Roberts, S.J.

Author_Institution

Dept. of Cognitive Neurology, Univ. Coll. London, UK

Volume

149

Issue

1

fYear

2002

fDate

2/1/2002 12:00:00 AM

Firstpage

33

Lastpage

41

Abstract

A variational Bayesian (VB) learning algorithm for parameter estimation and model-order selection in multivariate autoregressive (MAR) models is described. The use of structured priors in which subsets of coefficients are grouped together and constrained to be of a similar magnitude is explored. This allows MAR models to be more readily applied to high-dimensional data and to data with greater temporal complexity. The VB model order selection criterion is compared with the minimum description length approach. Results are presented on synthetic and electroencephalogram data

Keywords

Bayes methods; autoregressive processes; computational complexity; electroencephalography; learning systems; medical signal processing; parameter estimation; Bayesian multivariate AR models; Bayesian multivariate autoregressive models; cognitive-EEG data; electroencephalogram data; high-dimensional data; minimum description length; model order selection; multiple time series data; parameter estimation; sleep-EEG data; structured priors; synthetic data; temporal complexity; variational Bayesian learning algorithm;

fLanguage

English

Journal_Title

Vision, Image and Signal Processing, IEE Proceedings -

Publisher

iet

ISSN

1350-245X

Type

jour

DOI

10.1049/ip-vis:20020149

Filename

999168