时间：2018年3月21日 下午2：30-3：30
地点：明主1037会议室
题目：Bayesian selection of misspecified models is overconfident and may cause spurious posterior probabilities for phylogenetic trees
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 overconfidence 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.
朱天琪博士的主要研究方向是贝叶斯统计学和计算生物学。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。她的多项工作发表在领域内顶级杂志Systematic Biology，Molecular Biology & Evolution以及综合杂志PNAS上。2010年获得中国概率统计学会颁发的“宝洁优秀论文奖”，2015年入选中国科学院青年创新促进会会员。