Representing parametric probabilistic models tainted with imprecision
Fuzzy Sets and Systems
Unifying practical uncertainty representations -- I: Generalized p-boxes
International Journal of Approximate Reasoning
Practical representations of incomplete probabilistic knowledge
Computational Statistics & Data Analysis
Possibilistic information fusion using maximal coherent subsets
IEEE Transactions on Fuzzy Systems
The role of epistemic uncertainty in risk analysis
SUM'10 Proceedings of the 4th international conference on Scalable uncertainty management
Computers and Electronics in Agriculture
Information Sciences: an International Journal
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Random variability and imprecision are two distinct facets of the uncertainty affecting parameters that influence the assessment of risk. While random variability can be represented by probability distribution functions, imprecision (or partial ignorance) is better accounted for by possibility distributions (or families of probability distributions). Because practical situations of risk computation often involve both types of uncertainty, methods are needed to combine these two modes of uncertainty representation in the propagation step. A hybrid method is presented here, which jointly propagates probabilistic and possibilistic uncertainty. It produces results in the form of a random fuzzy interval. This paper focuses on how to properly summarize this kind of information; and how to address questions pertaining to the potential violation of some tolerance threshold. While exploitation procedures proposed previously entertain a confusion between variability and imprecision, thus yielding overly conservative results, a new approach is proposed, based on the theory of evidence, and is illustrated using synthetic examples