From Interval Methods of Representing Uncertainty to a General Description of Uncertainty

Measurements do not result in an exact value of the measured quantity even after the most accurate mea surement there is still some uncertainty about the ac tual value of the measured quantity Traditionally in science and engineering this uncertainty is character ized by a probability distribution however often we do not know this probability distribution exactly So to get a more adequate description of this uncertainty we must consider classes of possible probability distri butions A natural question is Are all possible classes needed for this description In this paper we show that even for simple situations we indeed need arbi trary closed convex classes of probability distributions Traditional Description of Uncertainty in Science and Engineering and its