Parallel Metropolis coupled Markov chain Monte Carlo for Bayesian phylogenetic inference | Zendy

Gautam Altekar | Zendy; Sandhya Dwarkadas | Zendy; John P. Huelsenbeck | Zendy; Fredrik Ronquist | Zendy

AI Assistant Blog Pricing

Home ZAIA Blog

Open Access

Parallel Metropolis coupled Markov chain Monte Carlo for Bayesian phylogenetic inference

Author(s) -

Gautam Altekar,

Sandhya Dwarkadas,

John P. Huelsenbeck,

Fredrik Ronquist

Publication year - 2004

Publication title -

bioinformatics

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 3.599

H-Index - 390

eISSN - 1367-4811

pISSN - 1367-4803

DOI - 10.1093/bioinformatics/btg427

Subject(s) - markov chain monte carlo , computer science , bayesian inference , metropolis–hastings algorithm , posterior probability , algorithm , bayesian probability , markov chain , inference , parallel computing , artificial intelligence , machine learning

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.

The content you want is available to Zendy users.

Already have an account? Click here to sign in.

Having issues? You can contact us here

Accelerating Research