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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

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To link to this item 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
Refereed:Yes
Divisions:Faculty of Science > School of Mathematical, Physical and Computational Sciences > Department of Mathematics and Statistics
ID Code:89497
Uncontrolled Keywords:Bayesian model comparison, Coalescent, Trans-dimensional Monte Carlo
Publisher:Springer

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