Measuring the Effect of Observations on Bayes Factors
提出一种度量单个观测值对对数贝叶斯因子影响的方法，通过比较包含和排除该观测值时的贝叶斯因子对数差，并给出正态样本、线性模型等场景的应用示例。
In this paper we consider a measure of the effect of single observations on a logarithmic Bayes factor defined via the difference in the logarithms of the Bayes factors conditional first on all the data and then omitting an observation. The measure is related to the conditional predictive ordinate. The form of the measure and examples of its use are presented for a variety of situations, normal samples, linear models, log linear models and the checking of distributional assumptions.