Deflating Trees: Improving Bayesian Branch-Length Estimates using Informed Priors

Abstract.—Prior distributions can have a strong effect on the results of Bayesian analyses. However, no general consensus exists for how priors should be set in all circumstances. Branch-length priors are of particular interest for phylogenetics, because they affect many parameters and biologically relevant inferences have been shown to be sensitive to the chosen prior distribution. Here, we explore the use of outside information to set informed branch-length priors and compare inferences from these informed analyses to those using default settings. For both the commonly used exponential and the newly proposed compound Dirichlet prior distributions, the incorporation of relevant outside information improves inferences for data sets that have produced problematic branch- and tree-length estimates under default settings.We suggest that informed priors are worthy of further exploration for phylogenetics. [Bayesian phylogenetics; branch lengths; prior choice.] Setting priors is a necessary step in any Bayesian analysis, but the best approach to choice of priors has been a contentious issue in phylogenetics as it has in many other fields of statistical inference (Efron 2013). Approaches vary widely and different priors have been shown to influence the results o