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  Adaptive Metropolis-coupled MCMC for BEAST 2

Müller​, N. F., & Bouckaert, R. (2020). Adaptive Metropolis-coupled MCMC for BEAST 2. PeerJ, 8: e9473. doi:10.7717/peerj.9473.

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Item Permalink: http://hdl.handle.net/21.11116/0000-0007-2DF7-1 Version Permalink: http://hdl.handle.net/21.11116/0000-0007-2DF8-0
Genre: Journal Article

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https://github.com/nicfel/CoupledMCMC (Supplementary material)
Description:
this adaptive Metropolis-coupled MCMC approach as an open source package licenced under GPL 3.0 to the Bayesian phylogenetics software BEAST 2

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 Creators:
Müller​, Nicola F., Author
Bouckaert, Remco1, Author              
Affiliations:
1Linguistic and Cultural Evolution, Max Planck Institute for the Science of Human History, Max Planck Society, ou_2074311              

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Free keywords: Bioinformatics tool, Bioinformatics, Computational Biology
 Abstract: 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.

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Language(s): eng - English
 Dates: 2020-09-16
 Publication Status: Published online
 Pages: 16
 Publishing info: -
 Table of Contents: Introduction

Methods and material
- Background
- Locally aware adaptive tuning of the temperature of heated chains
- Implementation
- Data availability and software
- Validation

Results
- Ergodicity of the adaptive Metropolis-coupled MCMC algorithm
- Automatic tuning of the temperature of heated chains
- The effect of heating on exploring the posterior

Discussion
 Rev. Type: Peer
 Identifiers: DOI: 10.7717/peerj.9473
Other: shh2707
 Degree: -

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Title: PeerJ
  Other : PeerJ
Source Genre: Journal
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Publ. Info: London [u.a.] : PeerJ Inc.
Pages: - Volume / Issue: 8 Sequence Number: e9473 Start / End Page: - Identifier: ISSN: 2167-8359
CoNE: https://pure.mpg.de/cone/journals/resource/2167-8359