Local sensitivity diagnostics for Bayesian models are described that are analogues of frequentist measures of leverage and influence. The diagnostics are simple to calculate using MCMC. A comparison between leverage and influence allows a general purpose definition of an outlier based on local perturbations. These outliers may indicate areas where the model does not fit well even if they do not influence model fit. The sensitivity diagnostics are closely related to predictive information criteria that are commonly used for Bayesian model choice. A diagnostic for prior-data conflict is proposed that may also be used to measure cross-conflict between different parts of the data.

翻译：本文描述了贝叶斯模型的局部敏感性诊断方法，这些方法是频率学派杠杆与影响度量的类比。该诊断方法可通过马尔可夫链蒙特卡罗方法简便计算。通过比较杠杆效应与影响程度，可以基于局部扰动给出异常值的通用定义。这些异常值可能指示模型拟合欠佳的区域，即使它们并未影响模型拟合效果。该敏感性诊断方法与常用于贝叶斯模型选择的预测信息准则密切相关。本文提出了一种先验-数据冲突诊断方法，该方法亦可用于度量数据不同部分之间的交叉冲突。