9th International Conference on Belief Functions

The Theory of belief functions, also referred to as Evidence theory or Dempster-Shafer theory is a well-established mathematical framework for reasoning and decision-making with uncertainty, with applications to machine learning, statistical inference, information fusion, knowledge representation, risk analysis, etc. It is an extension of probability theory and it has well understood connections with related frameworks such as random sets and imprecise probabilities. Recently, generalised evidence theory, based on random fuzzy sets, has been proposed as a unifying framework encompassing both Dempster-Shafer and possibility theories.

The biennial BELIEF conferences (sponsored by the Belief Functions and Applications Society) are dedicated to the confrontation of ideas, the reporting of recent achievements and the presentation of the wide range of applications of this theory. Previous editions of this conference series were held in Brest, France (2010); Compiègne, France (2012); Oxford, UK (2014); Prague, Czech Republic (2016); Compiègne, France (2018); Shanghai, China (2021); Paris, France (2022), and Belfast, UK (2024).

To support cross-fertilization among researchers working in different subfields of AI and related disciplines, tutorials and special sessions will be proposed, with emphasis on the links between machine learning, statistical inference and uncertain reasoning, including topics such as quantification of prediction uncertainty, fusion rules for ensemble learning, belief propagation in deep neural networks, links with explainable and symbolic AI, etc. Submissions of papers combining several of these topics, or more generally at the cross-road of belief functions and other AI methods or uncertainty theories, along with relevant applications, are welcome.

The Nineth International Conference on Belief Functions (BELIEF 2026) will be held in Nanjing, China, on October 19-21, 2026.