DocumentCode
1739133
Title
Variational Bayes for non-Gaussian 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
135
Abstract
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
Keywords
Bayes methods; estimation theory; learning (artificial intelligence); variational techniques; EEG; model order selection criteria; noise model order; non-Gaussian autoregressive models; robust estimation; synthetic data; variational Bayesian learning algorithm; Bayesian methods; Brain modeling; Cost function; Degradation; Electroencephalography; Gaussian noise; Gaussian processes; Least squares methods; Noise level; Noise robustness;
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.2000.889370
Filename
889370
Link To Document