Year: 2004
Type: article
Abstract: 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. The algorithm has been implemented using two popular parallel programming models: message passing and shared memory. Performance results indicate nearly linear speed improvement in both programming models for small and large data sets.
Source: Bioinformatics
Language: English
Subfield: Statistics and Probability
Field: Mathematics
Domain: Physical Sciences
Open Access status: bronze
Awards: CISE Research Infrastructure: Spatial Intelligence for Computer-Enhanced Interaction with Physical Environments, CAREER: Enhanced Software Distributed Shared Memory as a Compiler Target, ITR: Dynamically Tunable Clustered Multithreaded Architectures, Experimental Evolution of the Bacteriophage Group Leviviridae, NEW AWARD. FOLLOW-ON TO DE-FC 03-85DP40200 SCOEP=RESEARCH ININERTIAL CONFINEMENT FUSION;O PERATION & MGMT OF THE NATIONAL LASER USER'S FACILITY; UPGR, Bayesian Estimation of Host-Parasite Cospeciation, Transparent Middleware Support for Shared State on Heterogeneous Clusters, Extending Bayesian Phylogenetic Analysis, G9000273518, INTEGRATED SENSING: Distributed Architecture for a Voluble Intelligent Environment
OpenAlex ID: W2117443107