The well-calibrated Bayesian¶
Dawid, A. P. (1982). The well-calibrated Bayesian. Journal of the American Statistical Association, 77(379), 605-610.
Cited by¶
1 citation across 1 artifact.
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Primes¶
- Calibration
- The structural insight applies across domains: a physicist calibrating a spectrometer, a data scientist calibrating a classifier, and an organization calibrating performance metrics all follow the same template of reference-deviation-adjustment, as Dawid (1982) formalized in his account of the well-calibrated forecaster across substrates.
This sourceFoundational statistical formalization of calibration as a substrate-agnostic property: a forecaster is well calibrated if, of events assigned probability p, the long-run proportion that occur is p; proves a coherent Bayesian expects to be well calibrated.
- The structural insight applies across domains: a physicist calibrating a spectrometer, a data scientist calibrating a classifier, and an organization calibrating performance metrics all follow the same template of reference-deviation-adjustment, as Dawid (1982) formalized in his account of the well-calibrated forecaster across substrates.
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Registry ID ref:5b82f5c61035 · see in the full table