Adaptive Tree Proposals for Bayesian Phylogenetic Inference. (30th January 2021)

• Record Type: Journal Article
• Authors: Meyer, X
• Editors: Brown, Jeremy
• Is Part Of: Systematic biology. Volume 70:Number 5(2021)
• Page Start: 1015
• Page End: 1032
• Publication Date: 2021-01-30
• Subjects: Biology -- Classification -- Periodicals; Biology -- Periodicals; Biologie -- Classification -- Périodiques; Biologie -- Périodiques; 578.012
• DOI: 10.1093/sysbio/syab004
• Languages: English
• ISSNs: 1063-5157

Abstract

Abstract: Bayesian inference of phylogeny with Markov chain Monte Carlo plays a key role in the study of evolution. Yet, this method still suffers from a practical challenge identified more than two decades ago: designing tree topology proposals that efficiently sample tree spaces. In this article, I introduce the concept of adaptive tree proposals for unrooted topologies, that is, tree proposals adapting to the posterior distribution as it is estimated. I use this concept to elaborate two adaptive variants of existing proposals and an adaptive proposal based on a novel design philosophy in which the structure of the proposal is informed by the posterior distribution of trees. I investigate the performance of these proposals by first presenting a metric that captures the performance of each proposal within a mixture of proposals. Using this metric, I compare the performance of the adaptive proposals to the performance of standard and parsimony-guided proposals on 11 empirical data sets. Using adaptive proposals led to consistent performance gains and resulted in up to 18-fold increases in mixing efficiency and 6-fold increases in convergence rate without increasing the computational cost of these analyses. [Bayesian phylogenetic inference; Markov chain Monte Carlo; posterior probability distribution; tree proposals.]