Possibilistic Networks: Parameters Learning from Imprecise Data and Evaluation strategy
This work addresses the challenge of handling uncertain and imprecise information in multidisciplinary research, though it appears incremental as it builds on existing possibilistic network frameworks.
The paper tackles the problem of learning parameters for possibilistic networks from imprecise datasets containing multi-valued data, proposing a sampling process and a likelihood function based on random sets theory to parametrize these networks.
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.