Data from: Properties of Markov chain Monte Carlo performance across many empirical alignments -- part I

Sean M Harrington, Van Wishingrad & Robert C Thomson
Nearly all current Bayesian phylogenetic applications rely on Markov chain Monte Carlo (MCMC) methods to approximate the posterior distribution for trees and other parameters of the model. These approximations are only reliable if Markov chains adequately converge and sample from the joint posterior distribution. While several studies of phylogenetic MCMC convergence exist, these have focused on simulated datasets or select empirical examples. Therefore, much that is considered common knowledge about MCMC in empirical systems derives...
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