The variance-related part of the uncertainty is equivalent to the approximation uncertainty. The nonvariance-related part encompasses both the aleatoric and model uncertainties . In practice, it is often assumed that the hypothesis space H is correctly specified, meaning f ⋆ ∈ H. This is especially plausible in deep learning, where models are generally believed to have "universal approximation" capabilities . This assumption makes it possible to ignore model uncertainty, which is very difficult to capture . Thus, variance-and non-variance-related uncertainties are simply referred to as epistemic and aleatoric uncertainties, respectively.