PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Unified Inference for Variational Bayesian Linear Gaussian State-Space Models
David Barber and Silvia Chiappa
In: 20th Conference on Neural Information Processing Systems, 4-7 Dec 2006, Vancouver, Canada.

Abstract

Linear Gaussian State-Space Models are widely used and a Bayesian treatment of parameters is therefore of considerable interest. The approximate Variational Bayesian method applied to these models is an attractive approach, used successfully in applications ranging from acoustics to bioinformatics. The most challenging aspect of implementing the method is in performing inference on the hidden state sequence of the model. We show how to convert the inference problem so that standard and stable Kalman Filtering/Smoothing recursions from the literature may be applied. This is in contrast to previously published approaches based on Belief Propagation. Our framework both simplifies and unifies the inference problem, so that future applications may be easily developed. We demonstrate the elegance of the approach on Bayesian temporal ICA, with an application to finding independent components in noisy EEG signals.

EPrint Type:Conference or Workshop Item (Spotlight)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Learning/Statistics & Optimisation
Brain Computer Interfaces
Theory & Algorithms
ID Code:2238
Deposited By:Silvia Chiappa
Deposited On:07 October 2006