Paper Info
Title
Bayesian nonparametric hidden semi-Markov models
Abstract
There is much interest in the Hierarchical Dirichlet Process Hidden Markov Model (HDP-HMM) as a natural Bayesian nonparametric extension of the ubiquitous Hidden Markov Model for learning from sequential and time-series data. However, in many settings the HDP-HMM's strict Markovian constraints are undesirable, particularly if we wish to learn or encode non-geometric state durations. We can extend the HDP-HMM to capture such structure by drawing upon explicit-duration semi-Markov modeling, which has been developed mainly in the parametric non-Bayesian setting, to allow construction of highly interpretable models that admit natural prior information on state durations. In this paper we introduce the explicit-duration Hierarchical Dirichlet Process Hidden semi-Markov Model (HDP-HSMM) and develop sampling algorithms for efficient posterior inference. The methods we introduce also provide new methods for sampling inference in the finite Bayesian HSMM. Our modular Gibbs sampling methods can be embedded in samplers for larger hierarchical Bayesian models, adding semi-Markov chain modeling as another tool in the Bayesian inference toolbox. We demonstrate the utility of the HDP-HSMM and our inference methods on both synthetic and real experiments.
Year
DOI
Venue
2013
10.5555/2567709.2502602
Journal of Machine Learning Research
Keywords
Field
DocType
larger hierarchical bayesian model,inference method,hierarchical dirichlet process hidden,markov model,bayesian inference toolbox,efficient posterior inference,sampling inference,explicit-duration semi-markov modeling,finite bayesian hsmm,natural bayesian nonparametric extension
Data mining,Multidisciplinary approach,Markov model,Engineering,Bayesian nonparametrics
Journal
Volume
Issue
ISSN
14
1
1532-4435
Citations 
PageRank 
References 
11
0.91
9
Authors
2
Name
Order
Citations
PageRank
Matthew J. Johnson1885.35
Alan S. Willsky27466847.01