AI RESEARCH
Integral Imprecise Probability Metrics
arXiv CS.LG
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ArXi:2505.16156v3 Announce Type: replace-cross Quantifying differences between probability distributions is fundamental to statistics and machine learning, primarily for comparing statistical uncertainty. In contrast, epistemic uncertainty -- due to incomplete knowledge -- requires richer representations than those offered by classical probability. Imprecise probability (IP) theory offers such models, capturing ambiguity and partial belief.