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