Engineering and scientific measurement results of continuous quantities are not precise numbers but more or less non-precise. Therefore so-called non-precise numbers are necessary to describe measurement results. Non-precise numbers are special fuzzy subsets of the set of real numbers. For...

In standard Bayesian inference, a-priori distributions are assumed to be classical probability distributions. This is a topic of critical discussions because, in reality, a-priori information is usually more or less non-precise, i.e. fuzzy. Hence, a more general form of a-priori distributions...

A-priori knowledge in form of one exact probability distribution on the parameter space is questionable. For more general forms of a-priori information so-called non-precise a-priori densities are a suitable quantitative description. This kind of a-priori information can be used in a generalized...

Datenqualität, Genauigkeit bzw. Ungenauigkeit von Daten und anderen Informationen sind grundlegende Aspekte von Messungen und Beobachtungen, die quantitativ beschrieben werden müssen, um unrealistische Resultate von Analysen zu vermeiden. In vielen praktischen Anwendungen erscheint die Angabe...