title: 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.
authors.affiliation: Department of Biological Sciences, Louisiana State University, Baton Rouge, LA 70803, USA.
authors.forename: Bradley J
authors.initials: BJ
authors.lastname: Nelson
authors.affiliation: Department of Biological Sciences, Louisiana State University, Baton Rouge, LA 70803, USA.
authors.forename: John J
authors.initials: JJ
authors.lastname: Andersen
authors.affiliation: Department of Biological Sciences, Louisiana State University, Baton Rouge, LA 70803, USA jembrown@lsu.edu.
authors.forename: Jeremy M
authors.initials: JM
authors.lastname: Brown
mesh_terms: D000818:Animals; D001001:Anura / Q000145:classification; D001499:Bayes Theorem; D002965:Classification / Q000379:methods*; D003198:Computer Simulation; D049874:Corbicula / Q000145:classification; D003627:Data Interpretation, Statistical; D008116:Lizards / Q000145:classification; D010802:Phylogeny; D012984:Software
keywords: Bayesian phylogenetics; branch lengths; prior choice