Propositional and Relational Bayesian Networks Associated with Imprecise   and Qualitative Probabilistic Assesments
  This paper investigates a representation language with flexibility inspired by probabilistic logic and compactness inspired by relational Bayesian networks. The goal is to handle propositional and first-order constructs together with precise, imprecise, indeterminate and qualitative probabilistic assessments. The paper shows how this can be achieved through the theory of credal networks. New exact and approximate inference algorithms based on multilinear programming and iterated/loopy propagation of interval probabilities are presented; their superior performance, compared to existing ones, is shown empirically. 
Fabio Gagliardi Cozman, Cassio Polpo de Campos, Jaime Ide, Jose Carlos   Ferreira da Rocha
cs.AI