Bret Larget

TL;DR: The new version provides convergence diagnostics and allows multiple analyses to be run in parallel with convergence progress monitored on the fly, and provides more output options than previously, including samples of ancestral states, site rates, site dN/dS rations, branch rates, and node dates.

TL;DR: The Bayesian framework for analyzing aligned nucleotide sequence data to reconstruct phylogenies, assess uncertainty in the reconstructions, and perform other statistical inferences is developed and a Markov chain Monte Carlo sampler is employed to sample trees and model parameter values from their joint posterior distribution.

TL;DR: The Bayesian inference of phylogeny appears to possess advantages over the other methods in terms of ability to use complex models of evolution, ease of interpretation of the results, and computational efficiency.

TL;DR: A Markov chain to sample from the posterior distribution for a phylogenetic tree given sequence information from the corresponding set of organisms, a stochastic model for these data, and a prior distribution on the space of trees are derived.

TL;DR: A novel 2-stage Markov chain Monte Carlo (MCMC) method that first obtains independent Bayesian posterior probability distributions for individual genes using standard methods and introduces a one-parameter probability distribution to describe the prior distribution of concordance among gene trees.