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 using importance sampling to estimate properties of posteriors resulting from power-scaling the prior or likelihood. On this basis, we suggest a diagnostic that can indicate the presence of prior-data conflict or likelihood noninformativity and discuss limitations to this power-scaling approach. The approach can be easily included in Bayesian workflows with minimal effort by the model builder and we present an implementation in our new R package priorsense. We further demonstrate the workflow on case studies of real data using models varying in complexity from simple linear models to Gaussian process models.

翻译：确定后验对先验和似然扰动的敏感性是贝叶斯工作流的重要组成部分。我们提出了一种实用且计算高效的敏感性分析方法，利用重要性采样来估计对先验或似然进行幂缩放后产生的后验特性。基于此，我们提出了一种能够指示存在先验-数据冲突或似然非信息性的诊断方法，并讨论了这种幂缩放方法的局限性。该方法可以轻松地以最小工作量融入建模者的贝叶斯工作流中，我们在新开发的R包priorsense中提供了实现。我们进一步通过真实数据的案例研究展示了该工作流，使用的模型复杂度从简单线性模型到高斯过程模型不等。