Determining the sensitivity of the posterior to perturbations of the prior and likelihood is an important part of the Bayesian workflow. We introduce a practical and computationally efficient sensitivity analysis approach that is applicable to a wide range of models, based on power-scaling perturbations. We suggest a diagnostic based on this that can indicate the presence of prior-data conflict or likelihood noninformativity. The approach can be easily included in Bayesian workflows with minimal work by the model builder. We present the implementation of the approach in our new R package priorsense and demonstrate the workflow on case studies of real data.

翻译：确定后继者对扰动先前和可能性的敏感度是贝耶斯工作流程的一个重要部分。我们采用了一种实用和计算高效的敏感度分析方法,该方法适用于各种模型,基于功率尺度的扰动。我们建议在此基础上进行诊断,可以表明存在先前数据冲突或可能的非信息性。该方法可以很容易地纳入巴耶斯工作流程,模型构建者的工作极少。我们在新的R包预言中介绍了该方法的实施情况,并展示了真实数据案例研究的工作流程。