  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.
