Dirichlet process hidden Markov multiple change-point model

Stanley I.M. Ko, Terence T.L. Chong, Pulak Ghosh

Research output: Contribution to journalArticlepeer-review

34 Citations (Scopus)


This paper proposes a new Bayesian multiple change-point model which is based on the hidden Markov approach. The Dirichlet process hidden Markov model does not require the specification of the number of change-points a priori. Hence our model is robust to model specification in contrast to the fully parametric Bayesian model. We propose a general Markov chain Monte Carlo algorithm which only needs to sample the states around change-points. Simulations for a normal mean-shift model with known and unknown variance demonstrate advantages of our approach. Two applications, namely the coal-mining disaster data and the real United States Gross Domestic Product growth, are provided. We detect a single change-point for both the disaster data and US GDP growth. All the change-point locations and posterior inferences of the two applications are in line with existing methods.

Original languageEnglish
Pages (from-to)275-296
Number of pages22
JournalBayesian Analysis
Issue number2
Publication statusPublished - 2015
Externally publishedYes


  • Change-point
  • Dirichlet process
  • Hidden markov model
  • Markov chain monte carlo
  • Nonparametric bayesian

ASJC Scopus subject areas

  • Statistics and Probability
  • Applied Mathematics


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