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Semantics-Preserving Fusion of Structures of Probabilistic Graphical Models

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Foundations of Intelligent Systems

Part of the book series: Advances in Intelligent and Soft Computing ((AINSC,volume 122))

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Abstract

This paper gives an approach for fusing the directed acyclic graphs (DAGs) of BNs, an important and popular probabilistic graphical model (PGM). Considering conditional independence as the semantics implied in a BN, we focus on the DAG fusion while preserving the semantics in all participating BNs. Based on the concept and properties of Markov equivalence, we respectively give the algorithms for fusing equivalent and inequivalent common subgraphs of all participating BNs.

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References

  1. Pearl, J.: Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. Morgan Kaufmann, Palo Alto (1998)

    Google Scholar 

  2. Li, W., Liu, W., Yue, K.: Recovering the Global Structure from Multiple Local Bayesian Networks. International Journal of Artificial Intelligence Tools 17(6), 1067–1088 (2008)

    Article  Google Scholar 

  3. Zhang, Y., Yue, K., Yue, M., Liu, W.: An Approach for Fusing Bayesian Networks. Journal of Information and Computational Science 8(2), 194–201 (2011)

    Google Scholar 

  4. Matzkvich, I., Abramson, B.: The topological fusion of Bayes nets. In: Proc. of the 8th Conf. UAI, pp. 191–198 (1992)

    Google Scholar 

  5. Sagrado, J., Moral, S.: Qualitative combination of Bayesian networks. International Journal of Intelligent Systems 18, 237–249 (2003)

    Article  MATH  Google Scholar 

  6. Richardson, M., Domingos, P.: Learning with knowledge from multiple experts. In: Proc. of the 20th Conf. ICML, pp. 624–631 (2003)

    Google Scholar 

  7. Nielsen, S., Parsons, S.: An application of formal argumentation: Fusing Bayesian networks in multi-agent systems. Artificial Intelligence 171, 754–775 (2007)

    Article  MathSciNet  MATH  Google Scholar 

  8. Verma, T., Pearl, J.: Equivalence and synthesis of causal models. In: Proc. of the 6th Conf. UAI, pp. 220–227 (1990)

    Google Scholar 

  9. Chickering, D.: Learning equivalence classes of Bayesian-network structures. Journal of Machine Learning Research (2), 445–498 (2002)

    MathSciNet  MATH  Google Scholar 

  10. Andersson, S., Madigan, D., Perlman, M.: A characterization of Markov equivalence classes for acyclic digraphs. Annals of Statistics 25, 505–541 (1997)

    Article  MathSciNet  MATH  Google Scholar 

  11. Chickering, D.: A transformational characterization of Bayesian network structures. In: Proc. of the 11th Conf. UAI, pp. 87–98 (1995)

    Google Scholar 

  12. Meek, C.: Causal inference and causal explanation with background knowledge. In: Proc. of the 11th Conf. UAI, pp. 403–410 (1995)

    Google Scholar 

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Author information

Authors and Affiliations

  1. Department of Computer Science and Technology, School of Information Science and Engineering, Yunnan University, Kunming, P.R. China

    Kun Yue, Yunlei Zhu & Weiyi Liu

  2. Library of Southwest Forestry University, Kunming, P.R. China

    Kailin Tian

Authors
  1. Kun Yue
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  2. Yunlei Zhu
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  3. Kailin Tian
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  4. Weiyi Liu
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Editor information

Editors and Affiliations

  1. Department of Computer Science and Engineering, Shanghai Jiao Tong University, 800 Dongchuan Road, 200240, Shanghai, China

    Yinglin Wang

  2. School of Information Science and Technology, Southwest Jiaotong University, 610031, Chengdu, Sichuan Province, China

    Tianrui Li

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© 2011 Springer-Verlag Berlin Heidelberg

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Yue, K., Zhu, Y., Tian, K., Liu, W. (2011). Semantics-Preserving Fusion of Structures of Probabilistic Graphical Models. In: Wang, Y., Li, T. (eds) Foundations of Intelligent Systems. Advances in Intelligent and Soft Computing, vol 122. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-25664-6_8

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  • DOI: https://doi.org/10.1007/978-3-642-25664-6_8

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-25663-9

  • Online ISBN: 978-3-642-25664-6

  • eBook Packages: EngineeringEngineering (R0)

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