Bayesian Nonparametric Hidden Semi-Markov Models

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Provided by: Journal of Machine Learning Research (JMLR)
Topic: Big Data
Format: PDF
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 the authors wish to learn or encode non-geometric state durations. They 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.
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