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This paper examines methodology for performing Bayesian inference sequentially on a sequence of posteriors on spaces of different dimensions. For this, we use sequential Monte Carlo samplers, introducing the innovation of using deterministic transformations to move particles effectively between target distributions with different dimensions. This approach, combined with adaptive methods, yields an extremely flexible and general algorithm for Bayesian model comparison that is suitable for use in applications where the acceptance rate in reversible jump Markov chain Monte Carlo is low. We use this approach on model comparison for mixture models, and for inferring coalescent trees sequentially, as data arrives.

Original publication

DOI

10.1007/s11222-019-09903-y

Type

Journal article

Journal

Statistics and computing

Publication Date

01/2020

Volume

30

Pages

663 - 676

Addresses

1Department of Statistics, University of Warwick, Coventry, CV4 7AL UK.