Randle Christopher P, Pickett Kurt M
Department of Biological Sciences, Sam Houston State University, Huntsville, TX 77341-2116, USA.
Department of Biology, University of Vermont, 316 Marsh Life Science Building, Burlington, VT 05404, USA.
Cladistics. 2010 Oct;26(5):550-559. doi: 10.1111/j.1096-0031.2009.00301.x.
The objective Bayesian approach relies on the construction of prior distributions that reflect ignorance. When topologies are considered equally probable a priori, clades cannot be. Shifting justifications have been offered for the use of uniform topological priors in Bayesian inference. These include: (i) topological priors do not inappropriately influence Bayesian inference when they are uniform; (ii) although clade priors are not uniform, their undesirable influence is negated by the likelihood function, even when data sets are small; and (iii) the influence of nonuniform clade priors is an appropriate reflection of knowledge. The first two justifications have been addressed previously: the first is false, and the second was found to be questionable. The third and most recent justification is inconsistent with the first two, and with the objective Bayesian philosophy itself. Thus, there has been no coherent justification for the use of nonflat clade priors in Bayesian phylogenetics. We discuss several solutions: (i) Bayesian inference can be abandoned in favour of other methods of phylogenetic inference; (ii) the objective Bayesian philosophy can be abandoned in favour of a subjective interpretation; (iii) the topology with the greatest posterior probability, which is also the tree of greatest marginal likelihood, can be accepted as optimal, with clade support estimated using other means; or (iv) a Bayes factor, which accounts for differences in priors among competing hypotheses, can be used to assess the weight of evidence in support of clades. ©The Willi Hennig Society 2009.
客观贝叶斯方法依赖于构建反映无知的先验分布。当先验地认为拓扑结构具有同等可能性时，分支并非如此。对于在贝叶斯推断中使用均匀拓扑先验，人们给出了各种理由。这些理由包括：（i）当拓扑先验是均匀的时候，它们不会不适当地影响贝叶斯推断；（ii）尽管分支先验不是均匀的，但即使数据集很小时，它们的不良影响也会被似然函数抵消；以及（iii）非均匀分支先验的影响是对知识的适当反映。前两个理由之前已经讨论过：第一个是错误的，第二个被发现是有问题的。第三个也是最近提出的理由与前两个理由不一致，也与客观贝叶斯哲学本身不一致。因此，在贝叶斯系统发育学中使用非平坦分支先验没有连贯的理由。我们讨论了几种解决方案：（i）可以放弃贝叶斯推断，转而采用其他系统发育推断方法；（ii）可以放弃客观贝叶斯哲学，转而采用主观解释；（iii）具有最大后验概率的拓扑结构，也就是具有最大边际似然的树，可以被接受为最优的，使用其他方法估计分支支持度；或者（iv）可以使用贝叶斯因子，它考虑了竞争假设之间先验的差异，来评估支持分支的证据权重。©威利·亨尼希协会2009年。
