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A parallel algorithm for building possibilistic causal networks

Author
Sangüesa, R.; Cortes, U.; Gisolfi, A.
Type of activity
Journal article
Journal
International journal of approximate reasoning
Date of publication
1998-05
Volume
18
Number
3-4
First page
251
Last page
270
DOI
https://doi.org/10.1016/S0888-613X(98)00010-3 Open in new window
Abstract
Among the several representations of uncertainty, possibility theory allows also for the management of imprecision coming from data. Domain models with inherent uncertainty and imprecision can be represented by means of possibilistic causal networks that, the possibilistic counterpart of Bayesian belief networks. Only recently the definition of possibilistic network has been clearly stated and the corresponding inference algorithms developed. However, and in contrast to the corresponding develop...
Group of research
IDEAI-UPC - Intelligent Data Science and Artificial Intelligence Research Center
KEMLG - Knowledge Engineering and Machine Learning Group

Participants