Abstract
This paper considers the problem of the possibility representation of measurement uncertainty in the cases of information shortage: very few measurements, poor knowledge about the underlying probability distribution. After having related possibility distribution to probability confidence intervals, we present a procedure to build a possibility distribution for one measurement issued from an unimodal probability distribution. We consider then the addition of other measurements and more knowledge about the probability distribution. The key role of the uniform distribution as the probability distribution leading to the least specific possibility distribution is highlighted. The approach is compared and discussed versus the conventional one based on the Student distribution.
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Mauris, G. (2008). Inferring a Possibility Distribution from Very Few Measurements. In: Dubois, D., Lubiano, M.A., Prade, H., Gil, M.Á., Grzegorzewski, P., Hryniewicz, O. (eds) Soft Methods for Handling Variability and Imprecision. Advances in Soft Computing, vol 48. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-85027-4_12
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DOI: https://doi.org/10.1007/978-3-540-85027-4_12
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