A Domain-Theoretic Foundation for Imprecise Probability and Credal Sets

TL;DR

This paper introduces a domain-theoretic framework for imprecise probability reasoning, unifying logical, topological, and measure-theoretic approaches to handle uncertainty and credal sets.

Contribution

It develops a novel domain-theoretic foundation for imprecise probabilities, including Bayesian updating, conditional independence, and credal sets generated by iterated function systems.

Findings

Constructed a Scott-continuous mapping from credal sets to intervals.

Extended the theory to include conditional independence and logical predicates.

Introduced credal sets from iterated function systems with imprecise weights.

Abstract

We develop a domain-theoretic framework for imprecise probability reasoning and inference on general topological spaces with a countably based continuous lattice of open sets. We address two distinct forms of uncertainty: partial or incomplete event descriptions, and sets of probability distributions as represented by credal sets -- as well as their combination. Within this framework, we construct a theory of conditional probability and derive novel inference rules for performing Bayesian updating in the presence of these two complementary types of imprecision. These results are extended to a theory of conditional independence for imprecise probabilistic events. We also formulate logical predicates for conditional probability, Bayesian updating, and conditional independence, and we obtain the relevant soundness and completeness results. A key contribution is the construction of a Scott-continuous mapping from any credal set to the domain of intervals, providing a domain-theoretic realisation of classical results from capacity theory and Choquet integration. Finally, we introduce and study a new family of credal sets generated by iterated function systems with imprecise probability weights, broadening the scope of computationally tractable imprecise probabilistic models. The resulting computable framework unifies logical, topological, and measure-theoretic perspectives on uncertainty, supporting robust probabilistic inference under partial and set-valued information.