© 2004 Society of Systematic Biologists
Model Parameterization, Prior Distributions, and the General Time-Reversible Model in Bayesian Phylogenetics
1 Section of Integrative Biology, University of Texas 1 University Station C0930 Austin, Texas 78712, USA; E-mail: zwickl{at}mail.utexas.edu (D.J.Z.)
2 Department of Ecology and Evolutionary Biology, University of Connecticut 75 N. Eagleville Road, Unit 3043, Storrs, Connecticut 06269–3043, USA
Edited by Bruce Rannala: Associate Editor
| Abstract |
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Bayesian phylogenetic methods require the selection of prior probability distributions for all parameters of the model of evolution. These distributions allow one to incorporate prior information into a Bayesian analysis, but even in the absence of meaningful prior information, a prior distribution must be chosen. In such situations, researchers typically seek to choose a prior that will have little effect on the posterior estimates produced by an analysis, allowing the data to dominate. Sometimes a prior that is uniform (assigning equal prior probability density to all points within some range) is chosen for this purpose. In reality, the appropriate prior depends on the parameterization chosen for the model of evolution, a choice that is largely arbitrary. There is an extensive Bayesian literature on appropriate prior choice, and it has long been appreciated that there are parameterizations for which uniform priors can have a strong influence on posterior estimates. We here discuss the relationship between model parameterization and prior specification, using the general time-reversible model of nucleotide evolution as an example. We present Bayesian analyses of 10 simulated data sets obtained using a variety of prior distributions and parameterizations of the general time-reversible model. Uniform priors can produce biased parameter estimates under realistic conditions, and a variety of alternative priors avoid this bias.
Keywords: Bayesian phylogenetics; general time-reversible model; model parameterization; prior distributions
Received May 16, 2003; Revised July 29, 2003; Accepted August 13, 2004
3 Current Address: School of Computational Science and Information Technology (CSIT) and Department of Biological Science, Florida State University Tallahassee, Florida 32306–4120, USA
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