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Qualitative combination of Bayesian networks
Author(s) -
Del Sagrado José,
Moral Serafín
Publication year - 2003
Publication title -
international journal of intelligent systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.291
H-Index - 87
eISSN - 1098-111X
pISSN - 0884-8173
DOI - 10.1002/int.10086
Subject(s) - conditional independence , bayesian network , graphical model , computer science , artificial intelligence , representation (politics) , intersection (aeronautics) , machine learning , independence (probability theory) , domain (mathematical analysis) , bayesian probability , aggregate (composite) , domain knowledge , theoretical computer science , mathematics , statistics , mathematical analysis , materials science , engineering , politics , political science , law , composite material , aerospace engineering
Directed graphic models based on conditional independence provide a compact and concise representation of an expert's subjective belief about existing relationships between variables. Faced with the task of building a greater model, each expert must be a specialist in some subset of the whole knowledge domain. It would be desirable to aggregate the knowledge provided by those specialists under the form of graphical models into a single and more general representation. This article studies the consensus model that would be obtained by combining two graphs associated with Bayesian networks and applying the union and intersection of their independencies. © 2003 Wiley Periodicals, Inc.