Detecting and diagnosing prior and likelihood sensitivity with power-scaling
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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.
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