
Uncertainties can be estimated by placing a probability distributions over either the model parameters or model outputs. Epistemic uncertainty is modeled by placing a prior distribution over a model’s weights and then trying to capture how much these weights vary given some data. Aleatoric uncertainty, on the other hand, is modeled by placing a distribution over the output of the model.