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June 2015 Dirichlet Process Hidden Markov Multiple Change-point Model
Stanley I. M. Ko, Terence T. L. Chong, Pulak Ghosh
Bayesian Anal. 10(2): 275-296 (June 2015). DOI: 10.1214/14-BA910

Abstract

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.

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Stanley I. M. Ko. Terence T. L. Chong. Pulak Ghosh. "Dirichlet Process Hidden Markov Multiple Change-point Model." Bayesian Anal. 10 (2) 275 - 296, June 2015. https://doi.org/10.1214/14-BA910

Information

Published: June 2015
First available in Project Euclid: 2 February 2015

zbMATH: 1335.62052
MathSciNet: MR3420883
Digital Object Identifier: 10.1214/14-BA910

Keywords: Change-point , Dirichlet process , Hidden Markov model , Markov chain Monte Carlo , Nonparametric Bayesian

Rights: Copyright © 2015 International Society for Bayesian Analysis

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Vol.10 • No. 2 • June 2015
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