Sequential Monte Carlo with transformations.
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Embargo End Date
ICR Authors
Authors
Everitt, RG
Culliford, R
Medina-Aguayo, F
Wilson, DJ
Culliford, R
Medina-Aguayo, F
Wilson, DJ
Document Type
Journal Article
Date
2020-05-01
Date Accepted
2019-09-03
Abstract
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.
Citation
Statistics and Computing, 2020, 30 (3), pp. 663 - 676
Source Title
Statistics and Computing
Publisher
SPRINGER
ISSN
0960-3174
eISSN
1573-1375
1573-1375
1573-1375
Collections
Research Team
Cancer Genomics