Properties of Markov Chain Monte Carlo Performance across Many Empirical Alignments.

ABSTRACT: Nearly all current Bayesian phylogenetic applications rely on Markov chain Monte Carlo (MCMC) methods to approximate the posterior distribution for trees and other parameters of the model. These approximations are only reliable if Markov chains adequately converge and sample from the joint posterior distribution. Although several studies of phylogenetic MCMC convergence exist, these have focused on simulated data sets or select empirical examples. Therefore, much that is considered common knowledge about MCMC in empirical systems derives from a relatively small family of analyses under ideal conditions. To address this, we present an overview of commonly applied phylogenetic MCMC diagnostics and an assessment of patterns of these diagnostics across more than 18,000 empirical analyses. Many

SUBMITTER: Harrington SM 

PROVIDER: S-EPMC8042746 | biostudies-literature | 2021 Apr

REPOSITORIES: biostudies-literature