Hose, DominikHanss, Michael2019-07-042019-07-042019http://nbn-resolving.de/urn:nbn:de:bsz:93-opus-ds-104468http://elib.uni-stuttgart.de/handle/11682/10446http://dx.doi.org/10.18419/opus-10429In this contribution, we revisit Zadeh's Extension Principle in the context of imprecise probabilities and present two simple modifications to obtain meaningful results when using possibilistic calculus to propagate credal sets of probability distributions through models. It is demonstrated how these results facilitate the possibilistic solution of two benchmark problems in uncertainty quantification.eninfo:eu-repo/semantics/openAccess510preprint