Solutions to problems with imprecise data - an engineering perspective to generalized uncertainty models.

The present paper is about data corrupted by both aleatoric and epistemic uncertainty. A unification of randomness, which represents aleatoric uncertainty, and fuzziness, which represents epistemic uncertainty, is dicussed in detail. As a result, the main uncertainty characteristics, i.e., variability, incompleteness and imprecision, can be described. With a focus on engineering problems the aim is to bridge the imprecision of data to the decision making process. Suitable fields of applications are highlighted; remarks on the numerical treatment are given.