Maroua Haddad

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There has been an ever-increasing interest in multi-disciplinary 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(More)
Possibilistic networks are important tools for modelling and reasoning, especially in the presence of imprecise and/or uncertain information. These graphical models have been successfully used in several real applications. Since their construction by experts is complex and time consuming, several researchers have tried to learn them from data. In this(More)
Like Bayesian networks, possibilistic ones compactly encode joint uncertainty representations over a set of variables. Learning possibilistic networks from data in general and from imperfect or scarce data in particular, has not received enough attention. Indeed, only few works deal with learning the structure and the parameters of a possibilistic network(More)
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