Article
Calibration of Bayesian analyses including historical data: The perspective matters
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Published: | February 26, 2021 |
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Outline
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Bayesian methods yield posterior probabilities that are “calibrated” in a specific sense, which may also be expressed as a long-run frequency. We revisit the calibration concept and investigate the roles of informative and vague priors. In particular, we point out the implications for sequential analyses on accumulating data (utilizing informative priors from earlier stages) and robustification approaches in the context of analyses including historical data. We argue that Bayesian methods provide proper answers to particular questions. But the context needs to be kept in mind when interpreting posterior probabilities, especially when the focus is on long-run coverage probabilities.
The authors declare that they have no competing interests.
The authors declare that an ethics committee vote is not required.