Measuring Discordancy between Prior and Data
本文研究了度量有信息先验与观测数据之间不一致程度的方法，提出基于分数贝叶斯因子的度量，并应用于正态样本、二项样本和线性模型。
SUMMARY In this paper we consider possible measures of the degree of discordancy between a proper prior and observed data. A consideration of the problem when we gain complete knowledge of the parameters suggests that measures based on tail areas are unsuitable. A wish for invariance under transformations leads to a Bayes factor comparing the marginal distribution of the data with the proper prior with that with a non-informative prior. to overcome problems with improper priors some suggestions are compared. A measure based on a fractional Bayes factor is preferred. The method is illustrated for normal samples, binomial samples and linear models.