Data from: Using parsimony-guided tree proposals to accelerate convergence in Bayesian phylogenetic inference

Chi Zhang, John Huelsenbeck & Fredrik Ronquist
Sampling across tree space is one of the major challenges in Bayesian phylogenetic inference using Markov chain Monte Carlo (MCMC) algorithms. Standard MCMC tree moves consider small random perturbations of the topology, and select from candidate trees at random or based on the distance between the old and new topologies. MCMC algorithms using such moves tend to get trapped in tree space, making them slow in finding the globally most probable trees (known as `convergence')...
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