Yang, Z; (2016) Bayesian phylogenetic inference. In: Kliman, RM, (ed.) Encyclopaedia of Evolutionary Biology. (pp. 137-140). Elsevier

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Abstract

Bayesian statistics uses probability distributions to characterize uncertainties in parameters or models. Bayesian phylogeny reconstruction is a standard application of Bayesian model comparison in which the phylogenetic trees are different statistical models. The posterior probabilities of different trees are computed using Markov chain Monte Carlo (MCMC) algorithms. The Bayesian approach to phylogenetics has gained popularity in the past decade or two because of the development of powerful software programs, and because the framework allows one to implement sophisticated and realistic models of molecular sequence evolution, suitable for addressing important questions in evolutionary biology. Major areas of application include phylogenetic reconstruction, species tree estimation in presence of conflicting gene trees, estimation of species divergence times, and inference of natural selection affecting molecular evolution, to name just a few. This article provides an overview of the Bayesian approach to phylogenetics, including basic principles of Bayesian theory and computation.

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