Conditional probabilistic reasoning without conditional logic

Imaging is a class of non-Bayesian methods for the revision of probability density functions originally proposed as a semantics for conditional logic. Two of these revision functions, Standard Imaging and General Imaging, have successfully been applied to modelling information retrieval (IR). Due to the problematic nature of a“direct” implementation of Imaging revision functions, we propose their alternative implementation by representing the semantic structure that underlies them, in the language of a probabilistic (Bayesian) logic. Recasting these models of information retrieval in such a general-purpose knowledge representation (KR) tool, besides showing the potential of this “Bayesian” tool for the representation of non-Bayesian revision functions, paves the way to a possible integration of these models with other more KR-oriented models of IR, and to the exploitation of general purpose domain-knowledge.

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