经典方法与SHELF在先验启发式聚合中的比较
A Comparison of Prior Elicitation Aggregation using the Classical Method and SHELF
摘要：
从专家那里得到的主观贝叶斯先验分布可以聚集在一起形成组先验。本文比较聚合先验形成的等权重聚合，经典的方法，和谢菲尔德启发框架彼此和个人的专家先验，使用专家启发进行临床试验。使用适当的评分规则比较聚合方法和个体专家先验分布，以比较分布的信息性和校准。这三种聚合方法的性能优于单个专家，其中Sheffield启发式框架的性能最好。
英文摘要：
Subjective Bayesian prior distributions elicited from experts can be aggregated together to form group priors. This paper compares aggregated priors formed by Equal Weight Aggregation, the Classical Method, and the Sheffield Elicitation Framework to each other and individual expert priors, using an expert elicitation carried out for a clinical trial. Aggregation methods and individual expert prior distributions are compared using proper scoring rules to compare the informativeness and calibration of the distributions. The three aggregation methods outperform the individual experts, and the Sheffield Elicitation Framework performs best amongst them.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
