Adaptive Metropolis-coupled MCMC for BEAST 2.
Adaptive Metropolis-coupled MCMC for BEAST 2.
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作者：
Müller NF;Bouckaert RR
随着越来越复杂的模型用于研究进化模式，需要在这种模型下促进有效推理的方法。大都市耦合马尔可夫链蒙特卡罗（MCMC）长期以来一直用于加速系统发育分析和利用多核CPU。Metropolis耦合MCMC本质上并行运行多个MCMC链。除了一个冷链外，所有链都被加热，该冷链像常规MCMC链一样探索后验概率空间。这种加热允许链在系统发育状态空间中进行更大的跳跃。然后，加热的链可以用于为其他链（包括冷链）提出新的状态。使用这种方法的实际挑战之一是找到加热链的最佳温度以有效地探索状态空间。在这里，我们提供了一个自适应的大都会耦合MCMC方案贝叶斯遗传学，其中加热链之间的温差自动调整，以实现目标接受概率的状态被交换的个别链之间。我们首先通过比较自适应大都市耦合MCMC到MCMC在几个数据集上的推断来证明这种方法的有效性。然后，我们探讨大都市耦合MCMC提供的好处超过MCMC。我们实现了这种自适应的大都市耦合MCMC方法，作为GPL 3.0下许可的贝叶斯遗传学软件BEAST 2的开源软件包，可从。
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 .
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DOI：
10.1093/bioinformatics/btu201
发表时间：
2014-08-15
期刊：
Bioinformatics (Oxford, England)
影响因子：
--
作者：
Vaughan TG;Kühnert D;Popinga A;Welch D;Drummond AJ
通讯作者：
Drummond AJ
影响因子：
5.8
作者：
Mitragotri, Samir;Anissimov, Yuri G.;Roberts, Michael S.
通讯作者：
Roberts, Michael S.
影响因子：
6.5
作者：
Brown, Jeremy M.;Thomson, Robert C.
通讯作者：
Thomson, Robert C.
量化系统发育树空间的MCMC探索。
影响因子：
6.5
作者：
Whidden C;Matsen FA 4th
通讯作者：
Matsen FA 4th
影响因子：
2.4
作者：
Brooks, SP;Gelman, A
通讯作者：
Gelman, A