Bayesian Change Point Detection via a Generic Sparse Recursive Filter

A new sparse recursive filtering is suggested for the efficient inference of the joint posterior of the number of change points and their locations. The computational and storage costs of the sparse recursive filtering are quadratic to the number of uniformisation times generated by the uniformisation scheme, which can be scaled down to the number of change points. This new version of sparse recursive filtering is generally applicable for either conjugate or nonconjugate priors. It is also applicable when either cross‐segment dependence or cross‐segment independence occurs. Its good performance in some complicated circumstances is demonstrated through examples from robust Bayesian change point detection using ‐models, Bayesian change point detection with dependence cross‐segment, objective Bayesian change point detection and simulation studies, in which the marginal likelihood of the model is often difficult to obtain or intractable.

• Publication year

  2025

• Venue

  Applied Stochastic Models in Business and Industry

• Publication date

  2025-11-01

• Fields of study

  Not labeled

• Identifiers
  DOI 10.1002/asmb.70056
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

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