2008
NeurIPS
NeurIPS 2008
The Infinite Factorial Hidden Markov Model
Abstract
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.
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- Sequence Modeling
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- indian buffet process
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Hot Topic Early Bird
- hidden markov model
Authors
Topics
Artificial Intelligence > Core AI > Multi-Agent Systems
Artificial Intelligence > Bayesian & Probabilistic > Bayesian Learning
Artificial Intelligence > Bayesian & Probabilistic > Probabilistic Modeling
Machine Learning > Core Methods > Representation Learning
Machine Learning > Learning Types > Unsupervised Learning
Machine Learning > Optimization & Theory > Bayesian Inference
Machine Learning > Bayesian & Probabilistic > Bayesian Learning
Machine Learning > Bayesian & Probabilistic > Probabilistic Modeling
Machine Learning > Core Methods > Graphical Models
Artificial Intelligence > Bayesian & Probabilistic > Bayesian Inference
Machine Learning > Core Methods > Sequence Modeling
Machine Learning > Bayesian & Probabilistic > Nonparametric Bayesian