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...