Abstract
Although (probabilistic) inference in Bayesian networks has been well studied, the recent trend on extending Bayesian networks to model large and complex domains imposes new challenges on inference. In this paper, we suggest a method called path propagation that addresses these new challenges. The experimental results indicate that the proposed method achieves better performance than conventional method, especially for large Bayesian networks.
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© 2007 Springer Berlin Heidelberg
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Wu, D., He, L. (2007). Path Propagation for Inference in Bayesian Networks. In: Kobti, Z., Wu, D. (eds) Advances in Artificial Intelligence. Canadian AI 2007. Lecture Notes in Computer Science(), vol 4509. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72665-4_33
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DOI: https://doi.org/10.1007/978-3-540-72665-4_33
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-72664-7
Online ISBN: 978-3-540-72665-4
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