This allows for a collection of predictions to be generated through Monte Carlo methods, providing a statistical distribution that captures the inherent uncertainty in the predictions. By leveraging this probabilistic modeling approach, BNN offers a more nuanced understanding of prediction uncertainty, specifically epistemic uncertainty, which arises from the model's inherent limitations in learning from finite data. A key advantage of BNN is the marginalization process, which notably enhances the accuracy and calibration of contemporary deep neural networks. 59