Sequential Monte Carlo with transformations

Everitt, R. G., Culliford, R., Medina-Aguayo, F. and Wilson, D. J. (2020) Sequential Monte Carlo with transformations. Statistics and computing, 30 (3). pp. 663-676. ISSN 0960-3174 doi: 10.1007/s11222-019-09903-y

Abstract/Summary

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.

Item Type	Article
URI	https://reading-clone.eprints-hosting.org/id/eprint/89497
Identification Number/DOI	10.1007/s11222-019-09903-y
Refereed	Yes
Divisions	Science > School of Mathematical, Physical and Computational Sciences > Department of Mathematics and Statistics
Uncontrolled Keywords	Bayesian model comparison, Coalescent, Trans-dimensional Monte Carlo
Publisher	Springer
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