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.
