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Towards Jointree Propagation with Conditional Probability Distributions

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Rough Sets and Current Trends in Computing (RSCTC 2004)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 3066))

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Abstract

In this paper, we suggest a novel approach to jointree computation. Unlike all previous jointree methods, we propose that jointree computation should use conditional probability distributions rather than potentials. One salient feature of this approach is that the exact form of the messages to be transmitted throughout the network can be identified a priori. Consequently, irrelevant messages can be ignored, while relevant messages can be computed more efficiently. We discuss four advantages of our jointree propagation method.

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References

  1. Jensen, F.V., Lauritzen, S.L., Olesen, K.G.: Bayesian updating in causal probabilistic networks by local computations. Computational Statistics Quarterly 4, 269–282 (1990)

    MathSciNet  Google Scholar 

  2. Madsen, A.L., Jensen, F.V.: Lazy propagation: a junction tree inference algorithm based on lazy evaluation. Artificial Intelligence 113(1-2), 203–245 (1999)

    Article  MATH  MathSciNet  Google Scholar 

  3. Olesen, K.G., Madsen, A.L.: Maximal prime subgraph decomposition of Bayesian networks. IEEE Transactions on Systems, Man, and Cybernetics, B 32(1), 21–31 (2002)

    Article  Google Scholar 

  4. Pearl, J.: Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. Morgan Kaufmann, San Francisco (1988)

    Google Scholar 

  5. Shafer, G.: Probabilistic Expert Systems, Society for Industrial and Applied Mathematics (1996)

    Google Scholar 

  6. Wong, S.K.M., Butz, C.J., Wu, D.: On the implication problem for probabilistic conditional independency. IEEE Transactions on Systems, Man, and Cybernetics, A 30(6), 785–805 (2000)

    Article  Google Scholar 

  7. Wong, S.K.M., Butz, C.J.: Constructing the dependency structure of a multiagent probabilistic network. IEEE Transactions on Knowledge and Data Engineering 13(3), 395–415 (2001)

    Article  Google Scholar 

  8. Xiang, Y.: Probabilistic Reasoning in Multiagent Systems, Cambridge (2002)

    Google Scholar 

  9. Zhang, N.L., Poole, D.: Exploiting causal independence in Bayesian network inference. Journal of Artificial Intelligence Research 5, 301–328 (1996)

    MATH  MathSciNet  Google Scholar 

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Author information

Authors and Affiliations

  1. Department of Computer Science, University of Regina, Regina, Saskatchewan, S4S 0A2, Canada

    Cory J. Butz, Hong Yao & Howard J. Hamilton

Authors
  1. Cory J. Butz
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  2. Hong Yao
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  3. Howard J. Hamilton
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Editor information

Editors and Affiliations

  1. Shimane University, 89-1 Enya-cho Izumo, 6938501, Shimane, Japan

    Shusaku Tsumoto

  2. Systems Research Institute, Polish Academy of Sciences, 01-447, Warsaw, Poland

    Roman Słowiński

  3. The Linnaeus Centre for Bioinformatics, Uppsala University, Uppsala, Sweden

    Jan Komorowski

  4. Institute of Computer Science, Polish Academy of Sciences, 01–237, Warsaw, Poland

    Jerzy W. Grzymała-Busse

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© 2004 Springer-Verlag Berlin Heidelberg

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Butz, C.J., Yao, H., Hamilton, H.J. (2004). Towards Jointree Propagation with Conditional Probability Distributions. In: Tsumoto, S., Słowiński, R., Komorowski, J., Grzymała-Busse, J.W. (eds) Rough Sets and Current Trends in Computing. RSCTC 2004. Lecture Notes in Computer Science(), vol 3066. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-25929-9_44

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  • DOI: https://doi.org/10.1007/978-3-540-25929-9_44

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-22117-3

  • Online ISBN: 978-3-540-25929-9

  • eBook Packages: Springer Book Archive

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