Abstract
Probability-possibility transformation is a purely mechanical transformation of probabilistic support to possibilistic support and vice versa. In this paper, we apply the most common transformations to graphical models, i.e., Bayesian into possibilistic networks. We show that existing transformations are not appropriate to transform Bayesian networks to possibilistic ones since they cannot preserve the information incorporated in joint distributions. Therefore, we propose new consitency properties, exclusively useful for graphical models transformations.
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Ben Slimen, Y., Ayachi, R., Ben Amor, N. (2013). Probability-Possibility Transformation:. In: Masulli, F., Pasi, G., Yager, R. (eds) Fuzzy Logic and Applications. WILF 2013. Lecture Notes in Computer Science(), vol 8256. Springer, Cham. https://doi.org/10.1007/978-3-319-03200-9_13
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DOI: https://doi.org/10.1007/978-3-319-03200-9_13
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-03199-6
Online ISBN: 978-3-319-03200-9
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