γ is the predicted mean for the property and ν, α and β define predictions for the aleatoric uncertainty ( β α-1 ) and epistemic uncertainty ( β ν(α-1) ). Γ is the gamma function and λ is a hyperparameter controlling how much it should be prioritized that large-error samples have less evidence. The terms of the loss function represents two objectives; L N LL i optimizes model fitness while L R i penalizes predictions with high error by lowering evidence. The relative importance of the two terms is controlled by λ. We use λ = 0.2, which is the default suggested by Soleimany et al. . The epistemic (model) uncertainty is then obtained as