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。我们采用了实用和计算高效的敏感度分析方法,使用重要取样来估计前一级或可能性的功率缩放所产生的后继者属性。在此基础上,我们建议进行诊断,可以表明存在先前数据冲突或非信息性的可能性,并讨论这种权力缩放方法的局限性。这一方法很容易在模型建设者最小的努力下被纳入巴耶斯工作流程,我们在新的R包前言中介绍执行情况。我们进一步展示了使用简单线性模型和高斯进程模型复杂程度不同的模型进行实际数据案例研究的工作流程。