An Axiomatic Assessment of Entropy- and Variance-based Uncertainty Quantification in Regression
This work introduces a formal framework to represent uncertainty in continuous regression using a general parametric family of distributions and proposes six axioms to rigorously assess uncertainty measures. It compares entropy-based and variance-based quantification methods, revealing that while variance-based measures satisfy most axioms, entropy-based ones have limitations regarding non-negativity.