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.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: IEE Proceedings - Vision, Image, and Signal Processing
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
