title: Genealogical Working Distributions for Bayesian Model Testing with Phylogenetic Uncertainty.
abstract: Marginal likelihood estimates to compare models using Bayes factors frequently accompany Bayesian phylogenetic inference. Approaches to estimate marginal likelihoods have garnered increased attention over the past decade. In particular, the introduction of path sampling (PS) and stepping-stone sampling (SS) into Bayesian phylogenetics has tremendously improved the accuracy of model selection. These sampling techniques are now used to evaluate complex evolutionary and population genetic models on empirical data sets, but considerable computational demands hamper their widespread adoption. Further, when very diffuse, but proper priors are specified for model parameters, numerical issues complicate the exploration of the priors, a necessary step in marginal likelihood estimation using PS or SS. To avoid such instabilities, generalized SS (GSS) has recently been proposed, introducing the concept of "working distributions" to facilitate--or shorten--the integration process that underlies marginal likelihood estimation. However, the need to fix the tree topology currently limits GSS in a coalescent-based framework. Here, we extend GSS by relaxing the fixed underlying tree topology assumption. To this purpose, we introduce a "working" distribution on the space of genealogies, which enables estimating marginal likelihoods while accommodating phylogenetic uncertainty. We propose two different "working" distributions that help GSS to outperform PS and SS in terms of accuracy when comparing demographic and evolutionary models applied to synthetic data and real-world examples. Further, we show that the use of very diffuse priors can lead to a considerable overestimation in marginal likelihood when using PS and SS, while still retrieving the correct marginal likelihood using both GSS approaches. The methods used in this article are available in BEAST, a powerful user-friendly software package to perform Bayesian evolutionary analyses.
authors.affiliation: Department of Microbiology and Immunology, Rega Institute, KU Leuven-University of Leuven, Leuven, Belgium guy.baele@rega.kuleuven.be.
authors.forename: Guy
authors.initials: G
authors.lastname: Baele
authors.affiliation: Department of Microbiology and Immunology, Rega Institute, KU Leuven-University of Leuven, Leuven, Belgium.
authors.forename: Philippe
authors.initials: P
authors.lastname: Lemey
authors.affiliation: Department of Biomathematics and Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, CA 90095, USA Department of Biostatistics, School of Public Health, University of California, Los Angeles, CA 90095, USA.
authors.forename: Marc A
authors.initials: MA
authors.lastname: Suchard
mesh_terms: D001499:Bayes Theorem; D002965:Classification / Q000379:methods*; D003198:Computer Simulation; D019143:Evolution, Molecular; D008957:Models, Genetic; D010802:Phylogeny
keywords: Bayes factor; Bayesian inference; MCMC; Working distribution; coalescent model; marginal likelihood; phylogenetics