It is widely recognized, by the scienti?c and technical community that m- surements are the bridge between the empiric world
and that of the abstract concepts and knowledge. In fact, measurements provide us the quantitative knowledge about things
and phenomena. It is also widely recognized that the measurement result is capable of p- viding only incomplete information
about the actual value of the measurand, that is, the quantity being measured. Therefore, a measurement result - comes useful,
in any practicalsituation, only if a way is de?ned for estimating how incomplete is this information. The more recentdevelopment
of measurement science has identi?ed in the uncertainty concept the most suitable way to quantify how incomplete is the information
provided by a measurement result. However, the problem of how torepresentameasurementresulttogetherwithitsuncertaintyandpropagate
measurementuncertaintyisstillanopentopicinthe?eldofmetrology,despite many contributions that have been published in the literature
over the years. Many problems are in fact still unsolved, starting from the identi?cation of the best mathematical approach
for representing incomplete knowledge. Currently, measurement uncertainty is treated in a purely probabilistic way, because
the Theory of Probability has been considered the only available mathematical theory capable of handling incomplete information.
However, this approach has the main drawback of requiring full compensation of any systematic e?ect that a?ects the measurement
process. However, especially in many practical application, the identi?cation and compensation of all s- tematic e?ects is
not always possible or cost e?ective.