Coherent Predictive Inference under Exchangeability with Imprecise Probabilities (Extended Abstract)

Coherent Predictive Inference under Exchangeability with Imprecise Probabilities (Extended Abstract)

Gert de Cooman, Jasper De Bock, Márcio Alves Diniz

Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence
Journal track. Pages 4995-4999. https://doi.org/10.24963/ijcai.2017/709

Coherent reasoning under uncertainty can be represented in a very general manner by coherent sets of desirable gambles. This leads to a more general foundation for coherent (imprecise-)probabilistic inference that allows for indecision. In this framework, and for a given finite category set, coherent predictive inference under exchangeability can be represented using Bernstein coherent cones of multivariate polynomials on the simplex generated by this category set. We define an inference system as a map that associates a Bernstein coherent cone of polynomials with every finite category set. Inference principles can then be represented mathematically as restrictions on such maps, which allows us to develop a notion of conservative inference under such inference principles. We discuss, as particular examples, representation insensitivity and specificity, and show that there is an infinity of inference systems that satisfy these two principles.
Keywords:
Uncertainty in AI: Uncertainty Representations
Uncertainty in AI: Uncertainty in AI