论文标题
UQ验证的置信曲线：概率参考与Oracle
Confidence curves for UQ validation: probabilistic reference vs. oracle
论文作者
论文摘要
置信曲线用于不确定性验证，以评估大大不确定性（$ u_ {e} $）与大错误（$ e $）相关联。 Oracle曲线通常用作估计测试数据集质量的参考。 Oracle是一个完美，确定性的，错误的预测器，例如$ | e | = \ pm U_ {e} $，它与概率框架中非常不可能的错误分布相对应，并且无法就$ u_ {e} $的校准告知我们。我在这里建议通过概率参考曲线替换甲骨文，从更现实的场景中得出，其中应从具有标准偏差$ u_ {e} $的分布中随机绘制错误。概率曲线及其置信区间可以直接测试置信曲线的质量。与概率参考配对，置信度曲线可用于检查预测不确定性的校准和紧密度。
Confidence curves are used in uncertainty validation to assess how large uncertainties ($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 $|E|=\pm u_{E}$, 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.
