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Efficient Bayesian analysis of multiple changepoint models with dependence across segments.

Fearnhead, Paul and Liu, Zhen (2011) Efficient Bayesian analysis of multiple changepoint models with dependence across segments. Statistics and Computing, 21 (2). pp. 217-229. ISSN 0960-3174

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    Abstract

    We consider Bayesian analysis of a class of multiple changepoint models. While there are a variety of efficient ways to analyse these models if the parameters associated with each segment are independent, there are few general approaches for models where the parameters are dependent. Under the assumption that the dependence is Markov, we propose an efficient online algorithm for sampling from an approximation to the posterior distribution of the number and position of the changepoints. In a simulation study, we show that the approximation introduced is negligible. We illustrate the power of our approach through fitting piecewise polynomial models to data, under a model which allows for either continuity or discontinuity of the underlying curve at each changepoint. This method is competitive with, or out-performs, other methods for inferring curves from noisy data; and uniquely it allows for inference of the locations of discontinuities in the underlying curve.

    Item Type: Article
    Journal or Publication Title: Statistics and Computing
    Uncontrolled Keywords: Changepoint detection – Particle filters – Sequential Monte Carlo – Segmentation – Wavelets – Well-log
    Subjects: Q Science > QA Mathematics
    Departments: Faculty of Science and Technology > Mathematics and Statistics
    ID Code: 26279
    Deposited By: Prof Paul Fearnhead
    Deposited On: 28 Apr 2009 11:02
    Refereed?: Yes
    Published?: Published
    Last Modified: 09 Oct 2013 15:41
    Identification Number:
    URI: http://eprints.lancs.ac.uk/id/eprint/26279

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