Bayesian Semiparametric Multivariate Change Point Analysis

Stefano Peluso*, Chib Shiddharta, Mira Antonietta

*Corresponding author

Research output: Chapter in Book/Report/Conference proceedingConference contribution


We develop a general Bayesian semiparametric change-point model in which separate groups of parameters (for example, location and dispersion) can each follow a separate multiple change-point process, driven by time-dependent transition matrices among the latent regimes. The distribution of the observations within regimes dened by the various change-points is unknown and given by a Dirichlet process mixture prior. The prior-posterior analysis by Markov chain Monte Carlo techniques is developed on a multivariate forward-backward algorithm for sampling the various regime indicators.
Original languageEnglish
Title of host publicationBook of abstracts of ISBA 2016 World Meeting
Number of pages1
Publication statusPublished - 2016
EventISBA 2016 World Meeting - Cagliari
Duration: 13 Jun 201617 Jun 2016


ConferenceISBA 2016 World Meeting


  • Bayesian Nonparametrics
  • Change Point


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