Bayesian variance change point detection with credible sets

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• dc.contributor.author Cappello, Lorenzo
• dc.contributor.author Madrid Padilla, Oscar Hernan
• dc.date.accessioned 2025-05-08T11:20:12Z
• dc.date.available 2025-05-08T11:20:12Z
• dc.date.issued 2025
• dc.date.updated 2025-05-08T11:20:12Z
• dc.description Includes supplementary materials for the online appendix.
• dc.description.abstract This paper introduces a novel Bayesian approach to detect changes in the variance of a Gaussian sequence model, focusing on quantifying the uncertainty in the change point locations and providing a scalable algorithm for inference. We do that by framing the problem as a product of multiple single changes in the scale parameter. We fit the model through an iterative procedure similar to what is done for additive models. The novelty is that each iteration returns a probability distribution on time instances, which captures the uncertainty in the change point location. Leveraging a recent result in the literature, we can show that our proposal is a variational approximation of the exact model posterior distribution. We study the convergence of the algorithm and the change point localization rate. Extensive experiments in simulation studies and applications to biological data illustrate the performance of our method.
• dc.format.mimetype application/pdf
• dc.identifier.citation Cappello L, Madrid Padilla OH. Bayesian variance change point detection with credible sets. IEEE Trans Pattern Anal Mach Intell. 2025 Jun;47(6):4835-52. DOI: 10.1109/TPAMI.2025.3548012
• dc.identifier.doi http://dx.doi.org/10.1109/TPAMI.2025.3548012
• dc.identifier.issn 0162-8828
• dc.identifier.uri http://hdl.handle.net/10230/70335
• dc.language.iso eng
• dc.publisher Institute of Electrical and Electronics Engineers (IEEE)
• dc.relation.ispartof IEEE Transactions on Pattern Analysis and Machine Intelligence. 2025 Jun;47(6):4835-52
• dc.rights © 2025 The Authors. This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/.
• dc.rights.accessRights info:eu-repo/semantics/openAccess
• dc.rights.uri http://creativecommons.org/licenses/by/4.0/
• dc.subject.keyword Structural breaks
• dc.subject.keyword Variational inference
• dc.subject.keyword Localization rate
• dc.subject.keyword Approximate inference
• dc.title Bayesian variance change point detection with credible sets
• dc.type info:eu-repo/semantics/article
• dc.type.version info:eu-repo/semantics/publishedVersion