Journal article icon

Journal article

The infinite factorial hidden Markov model

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.

Actions


Authors


Journal:
Advances in Neural Information Processing Systems 21 - Proceedings of the 2008 Conference
Pages:
1697-1704
Publication date:
2009-01-01
URN:
uuid:f056a31a-2825-4e30-8b2b-1c08d95f0391
Source identifiers:
353242
Local pid:
pubs:353242
Language:
English

Terms of use


Metrics


Views and Downloads






If you are the owner of this record, you can report an update to it here: Report update to this record

TO TOP