PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

The Infinite Factorial Hidden Markov Model
Jurgen van Gael, Yee Whye Teh and Zoubin Ghahramani
Neural Information Processing Systems Volume 21, 2008.

Abstract

We introduce a new probability distribution over a potentially infinite number of binary Markov chains which we call the Markov Indian buffet process. This process extends the IBP to allow temporal dependencies in the hidden variables. We use this stochastic process to build a nonparametric extension of the factorial hidden Markov model. After constructing an inference scheme which combines slice sampling and dynamic programming we demonstrate how the infinite factorial hidden Markov model can be used for blind source separation.

PDF - Requires Adobe Acrobat Reader or other PDF viewer.
EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Learning/Statistics & Optimisation
ID Code:4287
Deposited By:Jurgen van Gael
Deposited On:07 March 2009