Algorithmic Advancements and Massive Parallelism for Large-Scale Datasets in Phylogenetic Bayesian Markov Chain Monte Carlo

Datasets used for the inference of the "tree of life" grow at unprecedented rates, thus inducing a high computational burden for analytic methods. First, we introduce a scalable software package that allows us to conduct state of the art Bayesian analyses on datasets of almost arbitrary size. Second, we derive a proposal mechanism for MCMC that is substantially more efficient than traditional branch length proposals. Third, we present an efficient algorithm for solving the rogue taxon problem.