Experimental comparative study of compilation-based inference in bayesian and possibilitic networks

Authors:
Raouia Ayachi;Nahla Ben Amor;Salem Benferhat
Affiliations:
LARODEC Laboratory, ISG, University of Tunis, Tunisia and CRIL-CNRS, University of Artois, France;LARODEC Laboratory, ISG, University of Tunis, Tunisia;CRIL-CNRS, University of Artois, France
Venue:
WILF'11 Proceedings of the 9th international conference on Fuzzy logic and applications
Year:
2011

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Abstract

Graphical models are important tools for representing and analyzing uncertain information. Diverse inference methods were developed for efficient computations in these models. In particular, compilation-based inference has recently triggered much research, especially in the probabilistic and the possibilistic frameworks. Even though the inference process follows the same principle in the two frameworks, it depends strongly on the specificity of each of them, namely in the interpretation of handled values (probability\possibility) and appropriate operators (*\min and +\max). This paper emphasizes on common points and unveils differences between the compilation-based inference process in the probabilistic and the possibilistic setting from a spatial viewpoint.