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Scalable Bayesian phylogenetics
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DOI：10.1098/rstb.2021.0242.png)
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Abstract
En 中文
Recent advances in Bayesian phylogenetics offer substantial computational savings to accommodate increased genomic sampling that challenges traditional inference methods. In this review, we begin with a brief summary of the Bayesian phylogenetic framework, and then conceptualize a variety of methods to improve posterior approximations via Markov chain Monte Carlo (MCMC) sampling. Specifically, we discuss methods to improve the speed of likelihood calculations, reduce MCMC burn-in, and generate better MCMC proposals. We apply several of these techniques to study the evolution of HIV virulence along a 1536-tip phylogeny and estimate the internal node heights of a 1000-tip SARS-CoV-2 phylogenetic tree in order to illustrate the speed-up of such analyses using current state-of-the-art approaches. We conclude our review with a discussion of promising alternatives to MCMC that approximate the phylogenetic posterior.This article is part of a discussion meeting issue 'Genomic population structures of microbial pathogens'.
Keywords:
Bayesian phylogenetics
scalable inference
online inference
Hamiltonian Monte Carlo
BEAST
adapative MCMC
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