The behavior of Metropolis-coupled Markov chains when sampling rugged phylogenetic distributions

Bayesian phylogenetic inference involves sampling from posterior distributions of trees, which sometimes exhibit local optima, or peaks, separated by regions of low posterior density. Markov chain Monte Carlo (MCMC) algorithms are the most widely used numerical method for generating samples from these posterior distributions, but they are susceptible to entrapment on individual optima in rugged distributions when they are unable to easily cross through or jump across regions of low posterior density. Ruggedness of posterior distributions can result from a variety of factors, including unmodeled variation in evolutionary processes and unrecognized variation in the true topology across sites or genes. Ruggedness can also become exaggerated when constraints are placed on topologies that require the presence or absence of particular bipartitions (often referred to as positive or negative constraints, respectively). These types of constraints are frequently employed when conducting tests of ...