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This paper investigates a representation language with flexibility inspiredby probabilistic logic and compactness inspired by relational Bayesiannetworks. The goal is to handle propositional and first-order constructstogether with precise, imprecise, indeterminate and qualitative probabilisticassessments. The paper shows how this can be achieved through the theory ofcredal networks. New exact and approximate inference algorithms based onmultilinear programming and iterated/loopy propagation of intervalprobabilities are presented; their superior performance, compared to existingones, is shown empirically.

Propositional and Relational Bayesian Networks Associated with Imprecise and Qualitative Probabilistic Assesments