报告题目：Nested sampling for frequentist computation: fast estimation of small p-values

　　报告人：Andrew Fowlie 副教授（南京师范大学）

　　报告人简介：Andrew Fowlie目前于南京师范大学任副教授。2013年于英国谢菲尔德大学取得博士毕业，师从Leszek Roszkowski教授。专长新物理唯象学，暗物质，数据分析与统计。目前主持青年科学基金一项，且是GAMBIT international global fitting collaboration。赴南京师范大学任职之前，曾在澳洲Monash大学以及爱沙尼亚KBFI从事博士后研究员工作。

　　报告摘要：We propose a novel method for computing pp-values based on nested sampling (NS) applied to the sampling space rather than the parameter space of the problem, in contrast to its usage in Bayesian computation. The computational cost of NS scales as \log^2{1/p}, which compares favorably to the 1/p scaling for Monte Carlo (MC) simulations. For significances greater than about 4σ in both a toy problem and a simplified resonance search, we show that NS requires orders of magnitude fewer simulations than ordinary MC estimates. This is particularly relevant for high-energy physics, which adopts a 5σ gold standard for discovery. We conclude with remarks on new connections between Bayesian and frequentist computation and possibilities for tuning NS implementations for still better performance in this setting.

　　报告时间：2021年7月9日下午13:30

　　报告地点：3号楼302会议室

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