文章基本信息

标题：Dirichlet Process Hidden Markov Multiple Change-point Model
本地全文：下载
作者：Stanley I. M. Ko ; Terence T. L. Chong ; Pulak Ghosh 等
期刊名称：Bayesian Analysis
印刷版ISSN：1931-6690
电子版ISSN：1936-0975
出版年度：2015
卷号：10
期号：2
页码：275-296
DOI：10.1214/14-BA910
语种：English
出版社：International Society for Bayesian Analysis
摘要：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.