标题： 好的、坏的和丑的：贝叶斯模型选择对系统发育树产生虚假的后验概率 显示英文标题

标题： The good, the bad, and the ugly: Bayesian model selection produces spurious posterior probabilities for phylogenetic trees

摘要： 贝叶斯方法在分析大型数据集时，被注意到会产生虚假的高后验概率，但对于这种过度自信的确切原因尚不清楚。 一般来说，对错误设定模型的贝叶斯选择性能理解得还不够深入，尽管这具有重要的科学意义，因为在实际数据分析中模型永远不可能是正确的。 在这里，我们描述了贝叶斯模型选择的渐近行为，并表明当竞争模型同样错误时，贝叶斯模型选择在大型数据集中表现出令人惊讶且极端的行为，全力支持一个模型而拒绝其他模型。 如果一个模型比另一个模型稍微不那么错误，当数据量增加时，较不错误的模型最终会获胜，但在变得可靠之前，该方法可能会变得过度自信。 我们认为这种极端行为可能是进化树虚假高后验概率的主要因素。 我们的结果对贝叶斯模型选择应用于评估对立科学假设的应用所具有的哲学意义还有待探索，类似情况下非贝叶斯方法的行为也尚未研究。 显示英文摘要

摘要： The Bayesian method is noted to produce spuriously high posterior probabilities for phylogenetic trees in analysis of large datasets, but the precise reasons for this over-confidence are unknown. In general, the performance of Bayesian selection of misspecified models is poorly understood, even though this is of great scientific interest since models are never true in real data analysis. Here we characterize the asymptotic behavior of Bayesian model selection and show that when the competing models are equally wrong, Bayesian model selection exhibits surprising and polarized behaviors in large datasets, supporting one model with full force while rejecting the others. If one model is slightly less wrong than the other, the less wrong model will eventually win when the amount of data increases, but the method may become overconfident before it becomes reliable. We suggest that this extreme behavior may be a major factor for the spuriously high posterior probabilities for evolutionary trees. The philosophical implications of our results to the application of Bayesian model selection to evaluate opposing scientific hypotheses are yet to be explored, as are the behaviors of non-Bayesian methods in similar situations.