Adaptive Tree Proposals for Bayesian Phylogenetic Inference.

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

SUBMITTER: Meyer X 

PROVIDER: S-EPMC8357345 | biostudies-literature | 2021 Aug

REPOSITORIES: biostudies-literature