  There has been an ever-increasing interest in multidisciplinary research on
representing and reasoning with imperfect data. Possibilistic networks present
one of the powerful frameworks of interest for representing uncertain and
imprecise information. This paper covers the problem of their parameters
learning from imprecise datasets, i.e., containing multi-valued data. We
propose in the rst part of this paper a possibilistic networks sampling
process. In the second part, we propose a likelihood function which explores
the link between random sets theory and possibility theory. This function is
then deployed to parametrize possibilistic networks.
