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Possibilistic conditional independence: A similarity-based measure and its application to causal network learning

Author
Sangüesa, R.; Cabós, J.; Cortes, U.
Type of activity
Journal article
Journal
International journal of approximate reasoning
Date of publication
1998-01
Volume
18
Number
1-2
First page
145
Last page
167
DOI
https://doi.org/10.1016/S0888-613X(98)00012-7 Open in new window
Abstract
A definition for similarity between possibility distributions is introduced and discussed as a basis for detecting dependence between variables by measuring the similarity degree of their respective distributions. This definition is used to detect conditional independence relations in possibility distributions derived from data. This is the basis for a new hybrid algorithm for recovering possibilistic causal networks. The algorithm POSS-CAUSE is presented and its applications discussed and compa...
Group of research
IDEAI-UPC - Intelligent Data Science and Artificial Intelligence Research Center
KEMLG - Knowledge Engineering and Machine Learning Group

Participants