Nonparametric Bayesian analysis for multi-site hidden Markov model

Dal Ho Kim, Aejung Jo, Yongku Kim

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

The hidden Markov model (HMM) provides an attractive framework for modeling long-term persistence in a variety of applications including pattern recognition. Unlike typical mixture models, hidden Markov states can represent the heterogeneity in data and it can be extended to a multivariate case using a hierarchical Bayesian approach. This article provides a nonparametric Bayesian modeling approach to the multi-site HMM by considering stick-breaking priors for each row of an infinite state transition matrix. This extension has many advantages over a parametric HMM. For example, it can provide more flexible information for identifying the structure of the HMM than parametric HMM analysis, such as the number of states in HMM. We exploit a simulation example and a real dataset to evaluate the proposed approach.

Original languageEnglish
Pages (from-to)4896-4907
Number of pages12
JournalCommunications in Statistics Part B: Simulation and Computation
Volume46
Issue number6
DOIs
StatePublished - 3 Jul 2017

Keywords

  • Bayesian analysis
  • Hierarchical modeling
  • Multi-site hidden Markov model
  • Stick-breaking prior

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