Maximum Uncertainty Procedures for Interval-Valued Probability Distributions

This work addresses uncertainty quantification in probabilistic modeling, particularly for interval-valued distributions, but appears incremental as it builds on existing concepts without claiming broad breakthroughs.

The paper tackles the problem of quantifying uncertainty and divergence for interval-valued probability distributions by introducing measures with desirable mathematical properties, and it presents a maximum uncertainty inference procedure for marginal interval distributions along with a reconstruction technique from projections.