Online Bayesian phylogenetic inference: Theoretical foundations via sequential Monte Carlo

Dinh, V; Darling, AE; Matsen, FA

Online Bayesian phylogenetic inference: Theoretical foundations via sequential Monte Carlo

Dinh, V Darling, AE

Matsen, FA

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Publication Type:: Journal Article
Citation:: Systematic Biology, 2018, 67 (3), pp. 503 - 517
Issue Date:: 2018-05-01

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Field	Value	Language
dc.contributor.author	Dinh, V	en_US
dc.contributor.author	Darling, AE https://orcid.org/0000-0003-2397-7925	en_US
dc.contributor.author	Matsen, FA	en_US
dc.date.available	2020-05-25T19:16:04Z
dc.date.issued	2018-05-01	en_US
dc.identifier.citation	Systematic Biology, 2018, 67 (3), pp. 503 - 517	en_US
dc.identifier.issn	1063-5157	en_US
dc.identifier.uri	http://hdl.handle.net/10453/124038
dc.description.abstract	© 2017 The Author(s). Phylogenetics, the inference of evolutionary trees from molecular sequence data such as DNA, is an enterprise that yields valuable evolutionary understanding of many biological systems. Bayesian phylogenetic algorithms, which approximate a posterior distribution on trees, have become a popular if computationally expensive means of doing phylogenetics. Modern data collection technologies are quickly adding newsequences to already substantial databases.With all current techniques for Bayesian phylogenetics, computation must start anew each time a sequence becomes available, making it costly to maintain an up-to-date estimate of a phylogenetic posterior. These considerations highlight the need for an online Bayesian phylogenetic method which can update an existing posterior with new sequences. Here, we provide theoretical results on the consistency and stability of methods for online Bayesian phylogenetic inference based on Sequential Monte Carlo (SMC) and Markov chain Monte Carlo. We first show a consistency result, demonstrating that the method samples from the correct distribution in the limit of a large number of particles. Next, we derive the first reported set of bounds on how phylogenetic likelihood surfaces change when new sequences are added. These bounds enable us to characterize the theoretical performance of sampling algorithms by bounding the effective sample size (ESS) with a given number of particles from below.We show that the ESS is guaranteed to grow linearly as the number of particles in an SMC sampler grows. Surprisingly, this result holds even though the dimensions of the phylogenetic model grow with each new added sequence.	en_US
dc.relation.ispartof	Systematic Biology	en_US
dc.relation.isbasedon	10.1093/sysbio/syx087	en_US
dc.subject.classification	Evolutionary Biology	en_US
dc.subject.mesh	Monte Carlo Method	en_US
dc.subject.mesh	Bayes Theorem	en_US
dc.subject.mesh	Phylogeny	en_US
dc.subject.mesh	Algorithms	en_US
dc.subject.mesh	Models, Biological	en_US
dc.subject.mesh	Classification	en_US
dc.title	Online Bayesian phylogenetic inference: Theoretical foundations via sequential Monte Carlo	en_US
dc.type	Journal Article
utslib.citation.volume	3	en_US
utslib.citation.volume	67	en_US
utslib.for	0604 Genetics	en_US
utslib.for	0603 Evolutionary Biology	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Science
pubs.organisational-group	/University of Technology Sydney/Strength - ithree - Institute of Infection, Immunity and Innovation
utslib.copyright.status	open_access
pubs.issue	3	en_US
pubs.publication-status	Published	en_US
pubs.volume	67	en_US

Abstract:

© 2017 The Author(s). Phylogenetics, the inference of evolutionary trees from molecular sequence data such as DNA, is an enterprise that yields valuable evolutionary understanding of many biological systems. Bayesian phylogenetic algorithms, which approximate a posterior distribution on trees, have become a popular if computationally expensive means of doing phylogenetics. Modern data collection technologies are quickly adding newsequences to already substantial databases.With all current techniques for Bayesian phylogenetics, computation must start anew each time a sequence becomes available, making it costly to maintain an up-to-date estimate of a phylogenetic posterior. These considerations highlight the need for an online Bayesian phylogenetic method which can update an existing posterior with new sequences. Here, we provide theoretical results on the consistency and stability of methods for online Bayesian phylogenetic inference based on Sequential Monte Carlo (SMC) and Markov chain Monte Carlo. We first show a consistency result, demonstrating that the method samples from the correct distribution in the limit of a large number of particles. Next, we derive the first reported set of bounds on how phylogenetic likelihood surfaces change when new sequences are added. These bounds enable us to characterize the theoretical performance of sampling algorithms by bounding the effective sample size (ESS) with a given number of particles from below.We show that the ESS is guaranteed to grow linearly as the number of particles in an SMC sampler grows. Surprisingly, this result holds even though the dimensions of the phylogenetic model grow with each new added sequence.

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http://hdl.handle.net/10453/124038