Data from: Deflating trees: improving Bayesian branch-length estimates using informed priors

Prior distributions can have a strong effect on the results of Bayesian analyses. However, no general consensus exists for how priors should be set in all circumstances. Branch-length priors are of particular interest for phylogenetics, because they affect many parameters and biologically relevant inferences have been shown to be sensitive to the chosen prior distribution. Here, we explore the use of outside information to set informed branch-length priors and compare inferences from these informed analyses to those using default settings. For both the commonly used exponential and the newly proposed compound Dirichlet prior distributions, the incorporation of relevant outside information improves inferences for datasets that have produced problematic branch- and tree-length estimates under default settings. We suggest that informed priors are worthy of further exploration for phylogenetics.

先验分布对贝叶斯分析（Bayesian analyses）的结果具有显著影响。然而，目前尚无普适性共识可指导所有场景下的先验分布设定。分支长度先验（branch-length priors）在系统发育学（phylogenetics）研究中备受关注，因其会影响诸多参数，且已有研究表明，与生物学相关的推断结果对所选用的先验分布极为敏感。本研究探讨利用外部信息设定信息性分支长度先验的方法，并将采用此类信息性先验的分析推断结果与采用默认设置的分析结果进行对比。针对常用的指数分布先验与新近提出的复合狄利克雷先验（compound Dirichlet prior）两类先验分布，纳入相关外部信息可有效改善那些在默认设置下得到存在偏差的分支长度与树长估计值的数据集的推断效果。我们认为，信息性先验在系统发育学研究中值得进一步探索。