Papers › Confidence curves for UQ validation: probabilistic reference vs. oracle
Confidence curves for UQ validation: probabilistic reference vs. oracle
Pascal Pernot
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Confidence curves are used in uncertainty validation to assess how large uncertainties (u_E) are associated with large errors (E). An oracle curve is commonly used as reference to estimate the quality of the tested datasets. The oracle is a perfect, deterministic, error predictor, such as |E|=±u_E, which corresponds to a very unlikely error distribution in a probabilistic framework and is unable unable to inform us on the calibration of u_E. I propose here to replace the oracle by a probabilistic reference curve, deriving from the more realistic scenario where errors should be random draws from a distribution with standard deviation u_E. The probabilistic curve and its confidence interval enable a direct test of the quality of a confidence curve. Paired with the probabilistic reference, a confidence curve can be used to check the calibration and tightness of prediction uncertainties.
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