• Corpus ID: 236493686

Detecting and diagnosing prior and likelihood sensitivity with power-scaling

@inproceedings{Kallioinen2021DetectingAD,
  title={Detecting and diagnosing prior and likelihood sensitivity with power-scaling},
  author={Noa Kallioinen and Topi Paananen and Paul-Christian Burkner and Aki Vehtari},
  year={2021}
}
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… 
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5 Citations
Background Citations
3
Methods Citations
3

5 Citations

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