Simple and Accurate Uncertainty Quantification from Bias-Variance Decomposition

Examples include medical diagnosis and selfdriving (Kennedy & O'Hagan, 2001) provides a more fine-grained vehicles. We propose a new method that categorization of uncertainty into six terms. Among them, is based directly on the bias-variance decomposition, the parameter and experimental uncertainties correspond where the parameter uncertainty is given by to the epistemic and aleatoric uncertainties in (Kendall & the variance of an ensemble divided by the number Gal, 2017), and the structural uncertainty corresponds to of members in the ensemble, and the aleatoric the missing model bias. For clarity, from now on we switch uncertainty plus the squared bias is estimated by to the uncertainty terminologies defined in (Kennedy & training a separate model that is regressed directly O'Hagan, 2001) for the rest of this paper.