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

NCJ Number
Date Published
January 2015
7 pages

This study explores how Bayesian branch-length estimates can be improved by using informed priors.


The authors of this study 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. The authors suggest that informed priors are worthy of further exploration for phylogenetics. 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. (Published Abstract Provided)

Date Published: January 1, 2015