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. 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.

朱天琪副研究员2012年于北京大学获得概率统计专业博士学位。同年进入中国科学院北京基因组研究所担任助理研究员，其间在英国伦敦大学、瑞典皇家理工大学进行短期访问。2016年进入中国科学院数学与系统科学研究院应用数学研究所工作。通过建立随机数学模型，使用统计方法和数值算法分析分子数据和进行进化推断。多项工作发表在领域内权威杂志Systematic Biology，Molecular Biology & Evolution以及综合杂志PNAS上。2019年入选中国科学院青年创新促进会优秀会员。