Exact posterior distributions over the segmentation space and model selection for multiple change-point detection problems

Guillem Rigaill (1, 2) , Stephane Robin (1)

1 MIA-Paris - Mathématiques et Informatique Appliquées
2 Département de Transfert

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.

Mots clés: change-point detection posterior distribution of change-points

Domaines: Sciences du Vivant [q-bio]

Guillem Rigaill, Stephane Robin. Exact posterior distributions over the segmentation space and model selection for multiple change-point detection problems. Proceedings of COMPSTAT'2010: 19th International Conference on Computational StatisticsParis France, August 22-27, 2010 Keynote, Invited and Contributed Papers, Springer - Verlag, 2010, 978-3-7908-2603-6. ⟨10.1007/978-3-7908-2604-3_57⟩. ⟨hal-01197523⟩