Assigning probability density functions in a context of information shortage

Abstract

In the context of experimental information shortage, uncertainty evaluation of a directly measured quantity involves obtaining its standard uncertainty as the standard deviation of an assigned probability density function (pdf) that is assumed to apply. In this article, we present a criterion to select the appropriate pdf associated with the estimate of a quantity by seeking that pdf which is the most probable among those which agree with the available information. As examples, we apply this criterion to assign the proper pdf to a measurand assuming that we know just its estimate, or both its estimate and its standard uncertainty. Our results agree with those obtained by applying the principle of maximum entropy to both situations.

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