Department of Zoology, University of Oxford, Oxford, OX1 3PS, UK.
Department of Infectious Disease Epidemiology, Imperial College London, London, W2 1PG, UK.
Nat Ecol Evol. 2017 Oct;1(10):1446-1454. doi: 10.1038/s41559-017-0280-x. Epub 2017 Sep 21.
Bayesian methods have become very popular in molecular phylogenetics due to the availability of user-friendly software implementing sophisticated models of evolution. However, Bayesian phylogenetic models are complex, and analyses are often carried out using default settings, which may not be appropriate. Here, we summarize the major features of Bayesian phylogenetic inference and discuss Bayesian computation using Markov chain Monte Carlo (MCMC), the diagnosis of an MCMC run, and ways of summarising the MCMC sample. We discuss the specification of the prior, the choice of the substitution model, and partitioning of the data. Finally, we provide a list of common Bayesian phylogenetic software and provide recommendations as to their use.
贝叶斯方法由于用户友好的软件的可用性，在分子系统发生学中变得非常流行，这些软件实现了复杂的进化模型。然而，贝叶斯系统发生模型非常复杂，分析通常是使用默认设置进行的，而这些默认设置可能并不合适。在这里，我们总结了贝叶斯系统发生推断的主要特征，并讨论了使用马尔可夫链蒙特卡罗（MCMC）进行贝叶斯计算、MCMC 运行的诊断以及对 MCMC 样本进行总结的方法。我们讨论了先验的指定、替代模型的选择以及数据的划分。最后，我们提供了一份常见贝叶斯系统发生软件的列表，并就其使用提供了一些建议。
