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Exact Posterior Distributions over the Segmentation Space and Model Selection for Multiple Change-Point Detection Problems

In: Proceedings of COMPSTAT'2010

Author

Listed:
  • G. Rigaill

    (AgroParisTech, UMR 518
    INRA, UMR 518
    Institut Curie, Département de Transfert)

  • E. Lebarbier

    (AgroParisTech, UMR 518
    INRA, UMR 518)

  • S. Robin

    (AgroParisTech, UMR 518
    INRA, UMR 518)

Abstract

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, non-asymptotic, explicit and tractable formulae for the posterior distribution of variables such as the number of change-points or their positions. We also derive a new selection criterion that accounts for the reliability of the results. All these results are based on an efficient strategy to explore the whole segmentation space, which can be very large. We illustrate our methodology on both simulated data and a comparative genomic hybridisation profile.

Suggested Citation

  • G. Rigaill & E. Lebarbier & S. Robin, 2010. "Exact Posterior Distributions over the Segmentation Space and Model Selection for Multiple Change-Point Detection Problems," Springer Books, in: Yves Lechevallier & Gilbert Saporta (ed.), Proceedings of COMPSTAT'2010, pages 557-564, Springer.
    • Handle: RePEc:spr:sprchp:978-3-7908-2604-3_57
    DOI: 10.1007/978-3-7908-2604-3_57
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