Parallel Metropolis coupled Markov chain Monte Carlo for Bayesian phylogenetic inference

MOTIVATION: Bayesian estimation of phylogeny is based on the posterior probability distribution of trees. Currently, the only numerical method that can effectively approximate posterior probabilities of trees is Markov chain Monte Carlo (MCMC). Standard implementations of MCMC can be prone to entrapment in local optima. Metropolis coupled MCMC [(MC)(3)], a variant of MCMC, allows multiple peaks in the landscape of trees to be more readily explored, but at the cost of increased execution time. RESULTS: This paper presents a parallel algorithm for (MC)(3). The proposed parallel algorithm retains the ability to explore multiple peaks in the posterior distribution of trees while maintaining a fast execution time.…

• NS
  National Science Foundation

  Awards: 0080124, 9702466, 0219848, 0075404, 0075406, 9988361, DEB-0075406, CCR-9702466, 0225413
• UD
  U.S. Department of Energy

  Awards: FC03-92SF19460, DE-FC03-92SF19460
• UO
  University of Rochester
• V
  Vetenskapsrådet