Sequential Monte Carlo with transformations.
Date
2020-05-01ICR Author
Author
Everitt, RG
Culliford, R
Medina-Aguayo, F
Wilson, DJ
Type
Journal Article
Metadata
Show full item recordAbstract
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.
Collections
Subject
Bayesian model comparison
Coalescent
Trans-dimensional Monte Carlo
Research team
Cancer Genomics
Language
eng
Date accepted
2019-09-03
License start date
2020-05-01
Citation
Statistics and Computing, 2020, 30 (3), pp. 663 - 676
Publisher
SPRINGER