Towards conservative inference in credal networks using belief functions: the case of credal chains
This work addresses uncertainty modeling for researchers in probabilistic reasoning, though it is incremental as it builds on previous methods and focuses on a subclass of networks.
The paper tackles belief inference in credal networks, specifically chains, by proposing a framework using Dempster-Shafer theory to propagate uncertainty and compute conservative intervals, with results showing computational efficiency and robust uncertainty representation.
This paper explores belief inference in credal networks using Dempster-Shafer theory. By building on previous work, we propose a novel framework for propagating uncertainty through a subclass of credal networks, namely chains. The proposed approach efficiently yields conservative intervals through belief and plausibility functions, combining computational speed with robust uncertainty representation. Key contributions include formalizing belief-based inference methods and comparing belief-based inference against classical sensitivity analysis. Numerical results highlight the advantages and limitations of applying belief inference within this framework, providing insights into its practical utility for chains and for credal networks in general.