Empirical Evaluation of Prior Beliefs about Frequencies: Methodology and a Case Study in Congenital Heart Disease
频率先验信念的实证检验：方法与先天性心脏病案例AI 解读 ↓
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频率先验信念的实证检验：方法与先天性心脏病案例
- 研究问题
- 随着离散变量样本陆续获得，如何检查专家给出的先验概率是否与数据相容，并及时识别需要修正的主观输入？
- 方法与思路
- 作者比较非序贯与序贯形式的Bayes因子和显著性检验，从观测数据预测概率的对数出发，构造用于折减专家判断的差异统计量。
- 材料陈述的贡献
- 摘要称两类比较方法具有共同的标准化基础；案例中的专家判断总体质量较高，作者据此建议建立自动监测与调整程序。
- 阅读关注点
- 应核对先验与数据比较统计量的定义、序贯监测规则、专家判断折减方式，以及案例评价标准和自动调整的实施条件。
- 阅读边界
- 所给OpenAlex索引摘要未注明稿件版本。摘要未提供判断质量的数值标准、序贯误差控制或调整细节，案例结论不能推广至所有专家系统。
原文摘要
We consider the problem of critiquing prior beliefs concerning the distribution of a discrete random variable in the light of a sequentially obtained sample. A topical application concerns a probabilistic expert system for the diagnosis of congenital heart disease, which requires specification of a large number of conditional probabilities that are initially imprecisely estimated by a suitable “expert.” These prior beliefs may be formally updated as data become available, but it would seem essential to contrast the original expert assessments with the data obtained to quickly identify inappropriate subjective inputs. We consider both Bayes factor and significance testing techniques for such a prior/data comparison, both in nonsequential and sequential forms. The common basis as alternative standardizations of the logarithm of the predictive ordinate of the observed data is emphasised, and a Bayesian discrepancy statistic with a variety of interpretations provides a formal means of discounting the expert's judgments in the light of the data. The judgments are found to be of generally high quality, and procedures for automatic monitoring and adaptation are recommended.