• Corpus ID: 237532357

How trustworthy is your tree? Bayesian phylogenetic effective sample size through the lens of Monte Carlo error

  title={How trustworthy is your tree? Bayesian phylogenetic effective sample size through the lens of Monte Carlo error},
  author={Andrew F. Magee and Michael D. Karcher and IV FrederickA.Matsen and Vladimir N. Minin},
Bayesian inference is a popular and widely-used approach to infer phylogenies (evolutionary trees). However, despite decades of widespread application, it remains difficult to judge how well a given Bayesian Markov chain Monte Carlo (MCMC) run explores the space of phylogenetic trees. In this paper, we investigate the Monte Carlo error of phylogenies, focusing on high-dimensional summaries of the posterior distribution, including variability in estimated edge/branch (known in phylogenetics as… 

Figures from this paper


Estimating the Effective Sample Size of Tree Topologies from Bayesian Phylogenetic Analyses
These methods are combined with two new diagnostic plots for assessing posterior samples of tree topologies, and provide new ways to assess the mixing and convergence of phylogenetic treetopologies in Bayesian MCMC analyses.
Properties of Markov Chain Monte Carlo Performance across Many Empirical Alignments
An overview of commonly applied phylogenetic MCMC diagnostics is presented and an assessment of patterns of these diagnostics across more than 18,000 empirical analyses show that the usage of models that include both Γ-distributed among-site rate variation and a proportion of invariable sites is not broadly problematic for MCMC convergence but is also unnecessary.
Quantifying MCMC Exploration of Phylogenetic Tree Space
It is shown conclusively that topological peaks do occur in Bayesian phylogenetic posteriors from real data sets as sampled with standard MCMC approaches, and the efficiency of Metropolis-coupled MCMC (MCMCMC) in traversing the valleys between peaks is investigated.
Convergence Assessment for Bayesian Phylogenetic Analysis using MCMC simulation
Different approaches for convergence assessment in phylogenetics are developed and test and it is shown that standard ESS computation can be applied to phylogenetic trees if the tree samples are converted into traces of absence/presence of splits.
Phylogenetic MCMC Algorithms Are Misleading on Mixtures of Trees
It is proved that the Markov chains take an exponentially long number of iterations to converge to the posterior distribution, which means that in cases of data containing potentially conflicting phylogenetic signals, phylogenetic reconstruction should be performed separately on each signal.
Efficiency of Markov chain Monte Carlo tree proposals in Bayesian phylogenetics.
It is found that proposals producing topology changes as a side effect of branch length changes (LOCAL and Continuous Change) consistently perform worse than those involving stochastic branch rearrangements (nearest neighbor interchange, subtree pruning and regrafting, tree bisection and reconnection, or subtree swapping).
Guided tree topology proposals for Bayesian phylogenetic inference.
This work investigates the performance of common MCMC proposal distributions in terms of median and variance of run time to convergence on 11 data sets, and introduces two new Metropolized Gibbs Samplers for moving through "tree space".
Systematic Exploration of the High Likelihood Set of Phylogenetic Tree Topologies
This paper presents an efficient parallelized method to map out the high likelihood set of phylogenetic tree topologies via systematic search, and shows that the normalized topology likelihoods are a useful proxy for the Bayesian posterior probability of those topologies.
Adaptive Tree Proposals for Bayesian Phylogenetic Inference
This article introduces the concept of tree topology proposals that adapt to the posterior distribution as it is estimated, and uses 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.
RWTY (R We There Yet): An R Package for Examining Convergence of Bayesian Phylogenetic Analyses.
Bayesian inference using Markov chain Monte Carlo (MCMC) has become one of the primary methods used to infer phylogenies from sequence data. Assessing convergence is a crucial component of these