Dirichlet Process Hidden Markov Multiple Change-point Model
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.
Cite
@article{arxiv.1505.01665,
title = {Dirichlet Process Hidden Markov Multiple Change-point Model},
author = {Stanley I. M. Ko and Terence T. L. Chong and Pulak Ghosh},
journal= {arXiv preprint arXiv:1505.01665},
year = {2015}
}
Comments
Published at http://dx.doi.org/10.1214/14-BA910 in the Bayesian Analysis (http://projecteuclid.org/euclid.ba) by the International Society of Bayesian Analysis (http://bayesian.org/)