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A parallel algorithm for building possibilistic causal networks

Autor
Sangüesa, R.; Cortes, U.; Gisolfi, A.
Tipus d'activitat
Article en revista
Revista
International journal of approximate reasoning
Data de publicació
1998-05
Volum
18
Número
3-4
Pàgina inicial
251
Pàgina final
270
DOI
https://doi.org/10.1016/S0888-613X(98)00010-3 Obrir en finestra nova
Resum
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...
Grup de recerca
IDEAI-UPC Intelligent Data Science and Artificial Intelligence
KEMLG - Grup d´Enginyeria del Coneixement i Aprenentatge Automàtic

Participants