Department of Entomology, Swedish Museum of Natural History, Box 50007, SE-104 05 Stockholm, Sweden; Department of Ecology and Environmental Science, Umeå University, SE-90187 Umeå, Sweden; and Department of Biodiversity Informatics, Swedish Museum of Natural History, Box 50007, SE-104 05 Stockholm, Sweden.
Syst Biol. 2013 Sep;62(5):660-73. doi: 10.1093/sysbio/syt029. Epub 2013 Apr 28.
We review Bayesian approaches to model testing in general and to the assessment of topological hypotheses in particular. We show that the standard way of setting up Bayes factor tests of the monophyly of a group, or the placement of a sample sequence in a known reference tree, can be misleading. The reason for this is related to the well-known dependency of Bayes factors on model-specific priors. Specifically, when testing tree hypotheses it is important that each hypothesis is associated with an appropriate tree space in the prior. This can be achieved by using appropriately constrained searches or by filtering trees in the posterior sample, but in a more elaborate way than typically implemented. If it is difficult to find the appropriate tree sets to be contrasted, then the posterior model odds may be more informative than the Bayes factor. We illustrate the recommended techniques using an empirical test case addressing the issue of whether two genera of diving beetles (Coleoptera: Dytiscidae), Suphrodytes and Hydroporus, should be synonymized. Our refined Bayes factor tests, in contrast to standard analyses, show that there is strong support for Suphrodytes nesting inside Hydroporus, and the genera are therefore synonymized.
我们回顾了贝叶斯方法在模型检验中的应用，特别是在拓扑假设评估中的应用。我们表明，在建立对一个群组的单系性或样本序列在已知参考树中的位置的贝叶斯因子检验时，标准方法可能会产生误导。其原因与贝叶斯因子对特定模型先验的依赖性有关。具体来说，在检验树假设时，重要的是每个假设都与先验中的适当树空间相关联。这可以通过使用适当约束的搜索或在后验样本中过滤树来实现，但需要比通常实现的方式更精细。如果难以找到要对比的适当树集，则后验模型概率可能比贝叶斯因子更具信息量。我们使用一个实证测试案例来说明推荐的技术，该案例涉及潜水甲虫（Coleoptera：Dytiscidae）的两个属，Suphrodytes 和 Hydroporus 是否应该合并。与标准分析相比，我们的精细化贝叶斯因子检验表明，Suphrodytes 嵌套在 Hydroporus 内有很强的支持，因此这两个属被合并了。
