We consider the analysis of sets of categorical sequences consisting of piecewise homogenous Markov segments. The sequences are assumed to be governed by a common underlying process with segments occurring in the same order for each sequence. Segments are defined by a set of unobserved changepoints where the positions and number of changepoints can vary from sequence to sequence. We propose a Bayesian framework for analyzing such data, placing priors on the locations of the changepoints and on the transition matrices and using Markov chain Monte Carlo (MCMC) techniques to obtain posterior samples given the data. Experimental results using simulated data illustrate how the methodology can be used for inference of posterior distributions for parameters and changepoints, as well as the ability to handle considerable variability in the locations of the changepoints across different sequences. We also investigate the application of the approach to sequential data from an application involving monsoonal rainfall patterns. Supplementary materials for this article are available online.


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Original Articles
Bayesian Detection of Changepoints in Finite-State Markov Chains for Multiple Sequences
Petter Arnesen
Department of Mathematical Sciences, Norwegian University of Science
and Technology, Trondheim, 7491, Norway (petterar@math.ntnu.no), Tracy Holsclaw
Department of Computer Science and the Department of Statistics,
University of California, Irvine, CA 92697 (tholscla@ams.ucsc.edu) & Padhraic Smyth Department of Computer Science, University of California, Irvine, CA 92697 (smyth@ics.uci.edu)
Pages 205-213 | Received 01 Aug 2014, Accepted author version posted online: 22 May 2015, Published online: 18 Apr 2016
Pages 205-213
Received 01 Aug 2014
Accepted author version posted online: 22 May 2015
Published online: 18 Apr 2016