Data from: Robustness of compound Dirichlet priors for Bayesian inference of branch lengths

We modified the phylogenetic program MrBayes 3.1.2 to incorporate the
compound Dirichlet priors for branch lengths proposed recently by Rannala,
Zhu, and Yang (2012. Tail paradox, partial identifiability and influential
priors in Bayesian branch length inference. Mol. Biol. Evol. 29:325-335.)
as a solution to the problem of branch-length overestimation in Bayesian
phylogenetic inference. The compound Dirichlet prior specifies a fairly
diffuse prior on the tree length (the sum of branch lengths) and uses a
Dirichlet distribution to partition the tree length into branch lengths.
Six problematic data sets originally analyzed by Brown, Hedtke, Lemmon,
and Lemmon (2010. When trees grow too long: investigating the causes of
highly inaccurate Bayesian branch-length estimates. Syst. Biol.
59:145-161) are reanalyzed using the modified version of MrBayes to
investigate properties of Bayesian branch-length estimation using the new
priors. While the default exponential priors for branch lengths produced
extremely long trees, the compound Dirichlet priors produced posterior
estimates that are much closer to the maximum likelihood estimates.
Furthermore, the posterior tree lengths were quite robust to changes in
the parameter values in the compound Dirichlet priors, for example, when
the prior mean of tree length changed over several orders of magnitude.
Our results suggest that the compound Dirichlet priors may be useful for
correcting branch-length overestimation in phylogenetic analyses of
empirical data sets.