Estimating Marginal Likelihood for Bayesian Phylogenetic Model Selection with Varying Topology

• Activity Number: 121
• Type: Topic Contributed
• Date/Time: Monday, July 30, 2012 : 8:30 AM to 10:20 AM
• Sponsor: Section on Bayesian Statistical Science
• Author(s): Rui Wu*+ and Paul Lewis and Yu Fan and Lynn Kuo and Ming-Hui Chen
• Companies: University of Connecticut and University of Connecticut and University of Connecticut and University of Connecticut and University of Connecticut
• Keywords: Bayesian phylogenetics ; marginal likelihood ; Monte Carlo Method ; varying topology

Abstract

Most work on Bayesian model selection for phylogenetic analysis has involved evaluating marginal likelihoods for various substitution models using a fixed tree topology. Estimating tree topology is the focus of many, if not most, phylogenetic analyses, however, and in such cases it is important to incorporate varying tree topology in the marginal likelihood evaluation. In this paper, we propose a new Monte Carlo method for computing the marginal likelihood using available MCMC output from the posterior distribution while tree topology varies. We illustrate the method with the simplest phylogenetic problem described in Yang (2000).