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Guided Tree Topology Proposals for Bayesian Phylogenetic Inference
Stockholm University, Faculty of Science, Department of Mathematics.
Number of Authors: 22012 (English)In: Systematic Biology, ISSN 1063-5157, E-ISSN 1076-836X, Vol. 61, no 1, p. 1-11Article in journal (Refereed) Published
Abstract [en]

Increasingly, large data sets pose a challenge for computationally intensive phylogenetic methods such as Bayesian Markov chain Monte Carlo (MCMC). Here, we investigate the performance of common MCMC proposal distributions in terms of median and variance of run time to convergence on 11 data sets. We introduce two new Metropolized Gibbs Samplers for moving through tree space. MCMC simulation using these new proposals shows faster average run time and dramatically improved predictability in performance, with a 20-fold reduction in the variance of the time to estimate the posterior distribution to a given accuracy. We also introduce conditional clade probabilities and demonstrate that they provide a superior means of approximating tree topology posterior probabilities from samples recorded during MCMC.

Place, publisher, year, edition, pages
2012. Vol. 61, no 1, p. 1-11
Keywords [en]
Bayesian inference, Gibbs sampling, Markov chain Monte Carlo, phylogenetics, posterior probability distribution, tree topology proposals
National Category
Biological Sciences
Identifiers
URN: urn:nbn:se:su:diva-162453DOI: 10.1093/sysbio/syr074ISI: 000299195700001PubMedID: 21828081OAI: oai:DiVA.org:su-162453DiVA, id: diva2:1267174
Available from: 2018-11-30 Created: 2018-11-30 Last updated: 2018-11-30Bibliographically approved

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