The intersection probability: betting with probability intervals
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Abstract (arXiv)
Probability intervals are an attractive tool for reasoning under uncertainty. Unlike belief functions, though, they lack a natural probability transformation to be used for decision making in a utility theory framework. In this paper we propose the use of the intersection probability, a transform derived originally for belief functions in the framework of the geometric approach to uncertainty, as the most natural such transformation. We recall its rationale and definition, compare it with other candidate representives of systems of probability intervals, discuss its credal rationale as focus of a pair of simplices in the probability simplex, and outline a possible decision making framework for probability intervals, analogous to the Transferable Belief Model for belief functions.
Key facts. Cited by 6 papers in the map (data through 14 Oct 2026). Builds on 8 of its 118 references that are in the map. First posted on arXiv 5 Jan 2022.
Builds on
8 of 118 references.
References · 8
- 2019Aleatoric and Epistemic Uncertainty in Machine Learning: An Introduction to Concepts and Methodscited by 913
- 2018Visions of a generalized probability theorycited by 13
- 2013Decision under Uncertaintycited by 11
- 2013The Dynamic of Belief in the Transferable Belief Model and Specialization-Generalization Matricescited by 5
- 2013A New Approach to Updating Beliefscited by 4
- 2013The Nature of the Unnormalized Beliefs Encountered in the Transferable Belief Modelcited by 3
- 2015Pignistic Probability Transforms for Mixes of Low- and High-Probability Eventscited by 3
- 2013Theoretical Foundations for Abstraction-Based Probabilistic Planningcited by 1
Built on by
Citing papers per year: 2024: 1; 2025: 1; 2026: 4.
6 papers.
- 2024Credal Wrapper of Model Averaging for Uncertainty Estimation in Classificationcited by 13
- 2025Epistemic Artificial Intelligence is Essential for Machine Learning Models to Truly 'Know When They Do Not Know'cited by 8
- 2026Random-Set Graph Neural Networkscited by 3
- 2026Credal Large Language Models for Semantic Commitment under Uncertaintycited by 2
- 2026A neurosymbolic Approach with Epistemic Deep Learning for Hierarchical Image Classificationcited by 1
- 2026Credal Machine Learning for Risk-Averse Decision Makingcited by 0
- 2026Credal Machine Learning for Risk-Averse Decision Making8 Oct 2026
- 2026Credal Large Language Models for Semantic Commitment under Uncertainty24 Aug 2026
- 2026Random-Set Graph Neural Networks12 May 2026
- 2026A neurosymbolic Approach with Epistemic Deep Learning for Hierarchical Image Classification11 May 2026
- 2025Epistemic Artificial Intelligence is Essential for Machine Learning Models to Truly 'Know When They Do Not Know'8 May 2025
- 2024Credal Wrapper of Model Averaging for Uncertainty Estimation in Classification23 May 2024