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Incorporating evidence in Bayesian networks with the select operator. (English) Zbl 1121.68365

Kégl, Balázs (ed.) et al., Advances in artificial intelligence. 18th conference of the Canadian Society for Computational Studies of Intelligence, Canadian AI 2005, Victoria, Canada, May 9–11, 2005. Proceedings. Berlin: Springer (ISBN 3-540-25864-7/pbk). Lecture Notes in Computer Science 3501. Lecture Notes in Artificial Intelligence, 297-301 (2005).
Summary: In this paper, we propose that the select operator in relational databases be adopted for incorporating evidence in Bayesian networks. This approach does not involve the construction of new evidence potentials, nor the associated computational costs of multiplying the evidence potentials into the knowledge base. The select operator also provides unified treatment of hard and soft evidence in Bayesian networks. Finally, some query optimization rules, involving the select operator implemented in relational databases, can be directly incorporated into probabilistic expert systems.
For the entire collection see [Zbl 1061.68003].

MSC:

68T05 Learning and adaptive systems in artificial intelligence
68P15 Database theory
68T35 Theory of languages and software systems (knowledge-based systems, expert systems, etc.) for artificial intelligence
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