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There has been an ever-increasing interest in multidisciplinary research onrepresenting and reasoning with imperfect data. Possibilistic networks presentone of the powerful frameworks of interest for representing uncertain andimprecise information. This paper covers the problem of their parameterslearning from imprecise datasets, i.e., containing multi-valued data. Wepropose in the rst part of this paper a possibilistic networks samplingprocess. In the second part, we propose a likelihood function which exploresthe link between random sets theory and possibility theory. This function isthen deployed to parametrize possibilistic networks.

Possibilistic Networks: Parameters Learning from Imprecise Data and Evaluation strategy