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.

中文翻译：

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BEAST 2 的自适应大都会耦合 MCMC

随着用于研究进化模式的模型越来越复杂，需要在此类模型下促进有效推理的方法。城域耦合马尔可夫链蒙特卡罗 (MCMC) 长期以来一直被用于加速系统发育分析和利用多核 CPU。Metropolis-coupled MCMC 本质上并行运行多个 MCMC 链。除了一个像常规 MCMC 链一样探索后验概率空间的冷链之外，所有链都被加热。这种加热允许链在系统发育状态空间中进行更大的跳跃。然后可以使用加热链为其他链（包括冷链）提出新状态。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探索状态空间。我们在这里为贝叶斯系统发育学提供了一种自适应 Metropolis 耦合 MCMC 方案，其中加热链之间的温差自动调整以实现单个链之间状态交换的目标接受概率。我们首先通过在几个数据集上比较自适应 Metropolis-coupled MCMC 与 MCMC 的推论来展示这种方法的有效性。然后，我们探索 Metropolis 耦合的 MCMC 比 MCMC 有哪些优势。我们将这种自适应 Metropolis 耦合 MCMC 方法实现为一个开源包，该包在 GPL 3.0 下许可给贝叶斯系统发育学软件 BEAST 2，可从 https://github.com/nicfel/CoupledMCMC 获得。其中加热链之间的温差会自动调整，以实现各个链之间状态交换的目标接受概率。我们首先通过在几个数据集上比较自适应 Metropolis-coupled MCMC 与 MCMC 的推论来展示这种方法的有效性。然后，我们探索 Metropolis 耦合的 MCMC 比 MCMC 有哪些优势。我们将这种自适应 Metropolis 耦合 MCMC 方法实现为一个开源包，该包在 GPL 3.0 下许可给贝叶斯系统发育学软件 BEAST 2，可从 https://github.com/nicfel/CoupledMCMC 获得。其中加热链之间的温差会自动调整，以实现各个链之间状态交换的目标接受概率。我们首先通过在几个数据集上比较自适应 Metropolis-coupled MCMC 与 MCMC 的推论来展示这种方法的有效性。然后，我们探索 Metropolis 耦合的 MCMC 比 MCMC 有哪些优势。我们将这种自适应 Metropolis 耦合 MCMC 方法实现为一个开源包，该包在 GPL 3.0 下许可给贝叶斯系统发育学软件 BEAST 2，可从 https://github.com/nicfel/CoupledMCMC 获得。