Adaptive Metropolis-coupled MCMC for BEAST 2

PeerJ

article

Nicola F. Müller¹  Remco R. Bouckaert⁴ 

[1] Department of Biosystems Science and Engineering;Swiss Institute of Bioinformatics;Fred Hutchinson Cancer Research Center;School of Computer Science, University of Auckland;Max Planck Institute for the Science of Human History

关键词: Bayesian;    Phylogenetics;    Phylodynamics;    Coalescent;    Parallel tempering;   

DOI  :  10.7717/peerj.9473

学科分类：社会科学、人文和艺术（综合）

来源: Inra

【 摘 要 】

With ever more complex models used to study evolutionary patterns, approaches that facilitate efficient inference under such models are needed. Metropolis-coupled Markov chain Monte Carlo (MCMC) has long been used to speed up phylogenetic analyses and to make use of multi-core CPUs. Metropolis-coupled MCMC essentially runs multiple MCMC chains in parallel. All chains are heated except for one cold chain that explores the posterior probability space like a regular MCMC chain. This heating allows chains to make bigger jumps in phylogenetic state space. The heated chains can then be used to propose new states for other chains, including the cold chain. One of the practical challenges using this approach, is to find optimal temperatures of the heated chains to efficiently explore state spaces. We here provide an adaptive Metropolis-coupled MCMC scheme to Bayesian phylogenetics, where the temperature difference between heated chains is automatically tuned to achieve a target acceptance probability of states being exchanged between individual chains. We first show the validity of this approach by comparing inferences of adaptive Metropolis-coupled MCMC to MCMC on several datasets. We then explore where Metropolis-coupled MCMC provides benefits over MCMC. We implemented this adaptive Metropolis-coupled MCMC approach as an open source package licenced under GPL 3.0 to the Bayesian phylogenetics software BEAST 2, available from https://github.com/nicfel/CoupledMCMC.

【 授权许可】

CC BY