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- Publisher
- Springer Journals
- Copyright
- Copyright © 2004 by Kluwer Academic Publishers
- Subject
- Computer Science; Artificial Intelligence (incl. Robotics); Mathematics, general; Computer Science, general; Complex Systems
- ISSN
- 1012-2443
- eISSN
- 1573-7470
- DOI
- 10.1023/B:AMAI.0000012874.65239.b0
- Publisher site
- See Article on Publisher Site
Abstract
Bayesian Knowledge Bases (BKB) are a rule-based probabilistic model that extends the well-known Bayes Networks (BN), by naturally allowing for context-specific independence and for cycles in the directed graph. We present a semantics for BKBs that facilitate handling of marginal probabilities, as well as finding most probable explanations.
Journal
Annals of Mathematics and Artificial Intelligence – Springer Journals
Published: Oct 5, 2004