Approximate Algorithms for Credal Networks with Binary Variables

This paper presents a family of algorithms for approximate inference in credal net-works (that is, models based on directed acyclic graphs and set-valued probabilities) that contain only binary variables. Such networks can represent incomplete or vague beliefs, lack of data, and disagreements among experts; they can also encode models based on belief functions and possibilistic measures. All algorithms for approximate inference in this paper rely on exact inferences in credal networks based on poly-trees with binary variables, as these inferences have polynomial complexity. We are inspired by approximate algorithms for Bayesian networks; thus the Loopy 2U al-gorithm resembles Loopy Belief Propagation, while the IPE and SV2U algorithms are respectively based on Localized Partial Evaluation and variational techniques. Key words: Credal networks, loopy belief propagation, variational methods, 2U algorith