Towards a Faster Inference Algorithm in Multiply Sectioned Bayesian Networks

  • Karen H. Jin
  • Dan Wu
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5032)

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.

Keywords

Bayesian Network Local Propagation Compilation Time Incoming Message Local Consistency 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    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)MathSciNetGoogle Scholar
  2. 2.
    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)MATHMathSciNetGoogle Scholar
  3. 3.
    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)Google Scholar
  4. 4.
    Pearl, J.: Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. Morgan Kaufmann Publishers, San Francisco, California (1988)Google Scholar
  5. 5.
    Shafer, G.: An axiomatic study of computation in hypertrees. School of Business Working Papers 232, University of Kansas (1991)Google Scholar
  6. 6.
    Wu, D., Jin, K.: Demystify the messages in the hugin architecture for probabilistic inference and its application. In: FLAIRS Conference, pp. 55–61 (2006)Google Scholar
  7. 7.
    Xiang, Y.: A probabilistic framework for cooperative multi-agent distributed interpretation and optimization of communication. Artificial Intelligence 87, 295–342 (1996)CrossRefMathSciNetGoogle Scholar
  8. 8.
    Xiang, Y.: Cooperative triangulation in msbns without revealing subnet structures. Networks 23, 1–21 (2001)CrossRefGoogle Scholar
  9. 9.
    Xiang, Y.: Probabilistic Reasoning in Multuagent Systems: A Graphical Models Approach, Cambridge (2002)Google Scholar
  10. 10.
    Xiang, Y.: Comparison of multiagent inference methods in multiply sectioned bayesian networks (2003)Google Scholar
  11. 11.
    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)Google Scholar
  12. 12.
    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)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Karen H. Jin
    • 1
  • Dan Wu
    • 1
  1. 1.School of Computer ScienceUniversity of WindsorWindsorCanada

Personalised recommendations