Evidential Regression is a regression technique that aims to model not only the relationship between inputs and target variables but also their uncertainties. These uncertainties have two origins: epistemic, which comes from the model's shortcomings in describing the relationship, and aleatoric, which is inherent to the dataset, such as noise. In the context of Deep Learning, a Deep Evidential Regression (DER) model is an NN capable of modeling a probability distribution given a set of training data, outputting the parameters of said distribution.