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.
Pannier, S., Waurick, M., Graf, W., & Kaliske, M. (2013). Solutions to problems with imprecise data - An engineering perspective to generalized uncertainty models. Mechanical Systems and Signal Processing, 37(1-2), 105-120. https://doi.org/10.1016/j.ymssp.2012.08.002