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Tytuł:
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An Annealed Sequential Monte Carlo Method for Bayesian Phylogenetics.
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Autorzy:
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Wang L; Department of Statistics and Actuarial Science, Simon Fraser University, Burnaby, British Columbia V5A 1S6, Canada.
Wang S; Department of Statistics and Actuarial Science, Simon Fraser University, Burnaby, British Columbia V5A 1S6, Canada.
Bouchard-Côté A; Department of Statistics, University of British Columbia, Vancouver, British Columbia V6T 1Z4, Canada.
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Źródło:
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Systematic biology [Syst Biol] 2020 Jan 01; Vol. 69 (1), pp. 155-183.
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Typ publikacji:
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Journal Article; Research Support, Non-U.S. Gov't
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Język:
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English
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Imprint Name(s):
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Publication: 2009- : Oxford : Oxford University Press
Original Publication: Washington, D.C., USA : Society of Systematic Biologists, [1992-
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MeSH Terms:
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Algorithms*
Phylogeny*
Classification/*methods
Bayes Theorem ; Monte Carlo Method ; Software
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Contributed Indexing:
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Keywords: Marginal likelihood; Sequential Monte Carlo; phylogenetics
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Entry Date(s):
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Date Created: 20190608 Date Completed: 20200115 Latest Revision: 20200115
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Update Code:
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20240105
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DOI:
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10.1093/sysbio/syz028
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PMID:
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31173141
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We describe an "embarrassingly parallel" method for Bayesian phylogenetic inference, annealed Sequential Monte Carlo (SMC), based on recent advances in the SMC literature such as adaptive determination of annealing parameters. The algorithm provides an approximate posterior distribution over trees and evolutionary parameters as well as an unbiased estimator for the marginal likelihood. This unbiasedness property can be used for the purpose of testing the correctness of posterior simulation software. We evaluate the performance of phylogenetic annealed SMC by reviewing and comparing with other computational Bayesian phylogenetic methods, in particular, different marginal likelihood estimation methods. Unlike previous SMC methods in phylogenetics, our annealed method can utilize standard Markov chain Monte Carlo (MCMC) tree moves and hence benefit from the large inventory of such moves available in the literature. Consequently, the annealed SMC method should be relatively easy to incorporate into existing phylogenetic software packages based on MCMC algorithms. We illustrate our method using simulation studies and real data analysis.
(© The Author(s) 2019. Published by Oxford University Press, on behalf of the Society of Systematic Biologists. All rights reserved. For permissions, please email: journals.permissions@oup.com.)
