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

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') and in estimating the correct proportions of the
different types of them (known as `mixing'). Here, we
introduce a new class of moves, which propose trees based on their
parsimony scores. The proposal distribution derived from the
parsimony scores is a quickly computable albeit rough approximation of the
conditional posterior distribution over candidate trees. We
demonstrate with simulations that parsimony-guided moves correctly sample
the uniform distribution of topologies from the prior. We then
evaluate their performance against standard moves using six challenging
empirical datasets, for which we were able to obtain accurate reference
estimates of the posterior using long MCMC runs, a mix of topology
proposals, and Metropolis coupling. On these datasets, ranging in
size from 357 to 934 taxa and from 1,740 to 5,681 sites, we find that
single chains using parsimony-guided moves usually converge an order of
magnitude faster than chains using standard moves. They also
exhibit better mixing, that is, they cover the most probable trees more
quickly. Our results show that tree moves based on quick and
dirty estimates of the posterior probability can significantly outperform
standard moves. Future research will have to show to what extent
the performance of such moves can be improved further by finding better
ways of approximating the posterior probability, taking the trade-off
between accuracy and speed into account.