Statistics and Computing

, Volume 22, Issue 4, pp 917–929 | Cite as

Exact posterior distributions and model selection criteria for multiple change-point detection problems



In segmentation problems, inference on change-point position and model selection are two difficult issues due to the discrete nature of change-points. In a Bayesian context, we derive exact, explicit and tractable formulae for the posterior distribution of variables such as the number of change-points or their positions. We also demonstrate that several classical Bayesian model selection criteria can be computed exactly. All these results are based on an efficient strategy to explore the whole segmentation space, which is very large. We illustrate our methodology on both simulated data and a comparative genomic hybridization profile.


Bayesian model selection change-point detection BIC DIC ICL posterior distribution of change-points posterior distribution of segments 


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Copyright information

© Springer Science+Business Media, LLC 2011

Authors and Affiliations

  1. 1.AgroParisTechUMR 518ParisFrance
  2. 2.INRAUMR 518ParisFrance
  3. 3.Département de TransfertInstitut CurieParisFrance
  4. 4.Bioinformatics and StatisticsNKI-AVLAmsterdamNetherlands

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