AAAI Publications, The Thirty-First International Flairs Conference

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Reliable Uncertain Evidence Modeling in Bayesian Networks by Credal Networks
Sabina Marchetti, Alessandro Antonucci

Last modified: 2018-05-10


A reliable modeling of uncertain evidence in Bayesian networks based on a set-valued quantification is proposed. Both soft and virtual evidences are considered. We show that evidence propagation in this setup can be reduced to standard updating in an augmented credal network, equivalent to a set of consistent Bayesian networks. A characterization of the computational complexity for this task is derived together with an efficient exact procedure for a subclass of instances. In the case of multiple uncertain evidences over the same variable, the proposed procedure can provide a set-valued version of the geometric approach to opinion pooling.


Bayesian networks; uncertain evidence; Jeffrey's rule; belief revision; credal networks

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