Abstract
Multiply sectioned Bayesian network(MSBN) is an extension of Bayesian network(BN) model for the support of flexible modelling in large and complex problem domains. However, current MSBN inference methods involve extensive intra-subnet(internal) and inter-subnet (external) message passings. In this paper, we present a new MSBN message passing scheme which substantially reduces the total number of message passings. By saving on both internal and external messages, our method improves the overall efficiency of MSBN inference compared with existing methods.
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References
Jensen, F.V., Lauritzen, S.L., Olesen, K.G.: Bayesian updating in causal probabilistic networks by local computation. Computational Statistics Quarterly 4, 269–282 (1990)
Lauritzen, S.L., Spiegelhalter, D.J.: Local computation with probabilities on graphical structures and their application to expert systems. Journal of the Royal Statistical Society 50, 157–244 (1988)
Lepar, V., Shenoy, P.P.: A comparison of Lauritzen-Spiegelhalter, Hugin, and Shenoy-Shafer architectures for computing marginals of probability distributions. In: Proceedings of the 14th Conference on Uncertainty in Artificial Intelligence (UAI 1998), pp. 328–337. Morgan Kaufmann, San Francisco (1998)
Pearl, J.: Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. Morgan Kaufmann Publishers, San Francisco, California (1988)
Shafer, G.: An axiomatic study of computation in hypertrees. School of Business Working Papers 232, University of Kansas (1991)
Wu, D., Jin, K.: Demystify the messages in the hugin architecture for probabilistic inference and its application. In: FLAIRS Conference, pp. 55–61 (2006)
Xiang, Y.: A probabilistic framework for cooperative multi-agent distributed interpretation and optimization of communication. Artificial Intelligence 87, 295–342 (1996)
Xiang, Y.: Cooperative triangulation in msbns without revealing subnet structures. Networks 23, 1–21 (2001)
Xiang, Y.: Probabilistic Reasoning in Multuagent Systems: A Graphical Models Approach, Cambridge (2002)
Xiang, Y.: Comparison of multiagent inference methods in multiply sectioned bayesian networks (2003)
Xiang, Y., Jensen, F.V., Chen, X.: Inference in multiply sectioned bayesian networks: Methods and performance comparison. IEEE Transaction on Systems, Man, and Cybernetics 36, 546–558 (2006)
Xiang, Y., Poole, D., Beddoes, M.P.: Multiply sectioned bayesian networks and junction forests for large knowledge based systems. Computational Intelligence 9, 171–220 (1993)
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© 2008 Springer-Verlag Berlin Heidelberg
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Jin, K.H., Wu, D. (2008). Towards a Faster Inference Algorithm in Multiply Sectioned Bayesian Networks. In: Bergler, S. (eds) Advances in Artificial Intelligence. Canadian AI 2008. Lecture Notes in Computer Science(), vol 5032. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-68825-9_15
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DOI: https://doi.org/10.1007/978-3-540-68825-9_15
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-68821-1
Online ISBN: 978-3-540-68825-9
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