Simulation-based Bayesian analysis for multiple changepoints

Details Simulation-based Bayesian analysis for multiple changepoints Simulation-based Bayesian analysis for multiple changepoints

2010-11-12

arxiv.org/abs/1011.2932v1

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Computation

17 pages

Scientific paper

This paper presents a Markov chain Monte Carlo method to generate approximate posterior samples in retrospective multiple changepoint problems where the number of changes is not known in advance. The method uses conjugate models whereby the marginal likelihood for the data between consecutive changepoints is tractable. Inclusion of hyperpriors gives a near automatic algorithm providing a robust alternative to popular filtering recursions approaches in cases which may be sensitive to prior information. Three real examples are used to demonstrate the proposed approach.

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