Parallel power posterior analyses for fast computation of marginal likelihoods in phylogenetics.

ABSTRACT: In Bayesian phylogenetic inference, marginal likelihoods can be estimated using several different methods, including the path-sampling or stepping-stone-sampling algorithms. Both algorithms are computationally demanding because they require a series of power posterior Markov chain Monte Carlo (MCMC) simulations. Here we introduce a general parallelization strategy that distributes the power posterior MCMC simulations and the likelihood computations over available CPUs. Our parallelization strategy can easily be applied to any statistical model despite our primary focus on molecular substitution models in this study. Using two phylogenetic example datasets, we demonstrate that the runtime of the marginal likelihood estimation can be reduced significantly even if only two CPUs are available

SUBMITTER: Hohna S 

PROVIDER: S-EPMC8570164 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature