© 2008 Society of Systematic Biologists
Efficiency of Markov Chain Monte Carlo Tree Proposals in Bayesian Phylogenetics
1 Department of Biological Science, Section Ecology and Evolution, Florida State University Tallahassee, Florida 32306-4120, USA; E-mail: lakner{at}scs.fsu.edu (C.L.)
2 School of Computational Science, Florida State University Tallahassee, Florida 32306-4120, USA
3 Department of Integrative Biology, 3060 VLSB 3140, University of California, Berkeley Berkeley, CA 94720-3140, USA
4 Departments of Botany and of Statistics, University of Wisconsin at Madison Wisconsin 53706, USA
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Abstract |
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The main limiting factor in Bayesian MCMC analysis of phylogeny is typically the efficiency with which topology proposals sample tree space. Here we evaluate the performance of seven different proposal mechanisms, including most of those used in current Bayesian phylogenetics software. We sampled 12 empirical nucleotide data sets—ranging in size from 27 to 71 taxa and from 378 to 2,520 sites—under difficult conditions: short runs, no Metropolis-coupling, and an oversimplified substitution model producing difficult tree spaces (Jukes Cantor with equal site rates). Convergence was assessed by comparison to reference samples obtained from multiple Metropolis-coupled runs. We find 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). Among the latter, moves that use an extension mechanism to mix local with more distant rearrangements show better overall performance than those involving only local or only random rearrangements. Moves with only local rearrangements tend to mix well but have long burn-in periods, whereas moves with random rearrangements often show the reverse pattern. Combinations of moves tend to perform better than single moves. The time to convergence can be shortened considerably by starting with a good tree, but this comes at the cost of compromising convergence diagnostics based on overdispersed starting points. Our results have important implications for developers of Bayesian MCMC implementations and for the large group of users of Bayesian phylogenetics software.
Keywords: Bayesian inference; Hastings ratio; Markov chain Monte Carlo; topology proposals
Received February 28, 2007; Revised June 25, 2007; Accepted September 27, 2007
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