The Infinite Factorial Hidden Markov Model

Part of Advances in Neural Information Processing Systems 21 (NIPS 2008)

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Jurgen Gael, Yee Teh, Zoubin Ghahramani


We introduces 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 working out 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.