Approximation and model uncertainties together form the epistemic uncertainty . The key distinction between aleatoric and epistemic uncertainty is that epistemic uncertainty can, in principle, be reduced with additional information-such as better background knowledge to address model bias or more training data to reduce model variance. In contrast, aleatoric uncertainty is typically regarded as irreducible, as it stems from the inherent randomness in the data .