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Learning causal networks from data: a survey and a new algorithm for recovering possibilistic causal networks

Autor
Sangüesa, R.; Cortes, U.
Tipus d'activitat
Article en revista
Revista
AI communications: the european journal of artificial intelligence
Data de publicació
1997-03
Volum
10
Número
1
Pàgina inicial
31
Pàgina final
61
Resum
Causal concepts play a crucial role in many reasoning tasks. Organised as a model revealing the causal structure of a domain, they can guide inference through relevant knowledge. This is an especially difficult kind of knowledge to acquire, so some methods for automating the induction of causal models from data have been put forth. Here we review those that have a graph representation. Most work has been done on the problem of recovering belief nets from data but some extensions are appearing th...
Grup de recerca
IDEAI-UPC Intelligent Data Science and Artificial Intelligence
KEMLG - Grup d´Enginyeria del Coneixement i Aprenentatge Automàtic

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