Bayesian phylogeny analysis via stochastic approximation Monte Carlo

Sooyoung Cheon, Faming Liang

Research output: Contribution to journalArticlepeer-review

6 Scopus citations

Abstract

Monte Carlo methods have received much attention in the recent literature of phylogeny analysis. However, the conventional Markov chain Monte Carlo algorithms, such as the Metropolis-Hastings algorithm, tend to get trapped in a local mode in simulating from the posterior distribution of phylogenetic trees, rendering the inference ineffective. In this paper, we apply an advanced Monte Carlo algorithm, the stochastic approximation Monte Carlo algorithm, to Bayesian phylogeny analysis. Our method is compared with two popular Bayesian phylogeny software, BAMBE and MrBayes, on simulated and real datasets. The numerical results indicate that our method outperforms BAMBE and MrBayes. Among the three methods, SAMC produces the consensus trees which have the highest similarity to the true trees, and the model parameter estimates which have the smallest mean square errors, but costs the least CPU time. © 2009 Elsevier Inc. All rights reserved.
Original languageEnglish (US)
Pages (from-to)394-403
Number of pages10
JournalMolecular Phylogenetics and Evolution
Volume53
Issue number2
DOIs
StatePublished - Nov 2009
Externally publishedYes

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