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Dempster Conditioning and Conditional Independence in Evidence Theory

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AI 2005: Advances in Artificial Intelligence (AI 2005)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 3809))

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Abstract

In this paper, we discuss the conditioning issue in D-S evidence theory in multi-dimensional space. Based on Dempster conditioning, Bayes’ rule and product rule, which are similar to that in probability theory, are presented in this paper. Two kinds of conditional independence called weak conditional independence and strong conditional independence are introduced, which can significantly simplify the inference process when evidence theory is applied to practical application.

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References

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Author information

Authors and Affiliations

  1. College of Computer Science, Zhejiang University, Hangzhou, Zhejiang Province, 310027, P.R. China

    Yongchuan Tang

  2. College of Economics, Zhejiang University, Hangzhou, Zhejiang Province, 310027, P.R. China

    Jiacheng Zheng

Authors
  1. Yongchuan Tang
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  2. Jiacheng Zheng
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Editor information

Editors and Affiliations

  1. Guangxi Normal University, College of CS and IT, Guilin, China, and University of Technology, Faculty of Engineering and Information Technology, Sydney, Australia

    Shichao Zhang

  2. Department of Electrical and Computer Systems Engineering, Monash University, 3800, Melbourne, Victoria, Australia

    Ray Jarvis

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© 2005 Springer-Verlag Berlin Heidelberg

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Tang, Y., Zheng, J. (2005). Dempster Conditioning and Conditional Independence in Evidence Theory. In: Zhang, S., Jarvis, R. (eds) AI 2005: Advances in Artificial Intelligence. AI 2005. Lecture Notes in Computer Science(), vol 3809. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11589990_88

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  • DOI: https://doi.org/10.1007/11589990_88

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-30462-3

  • Online ISBN: 978-3-540-31652-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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