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An Alternative Proof That Exact Inference Problem in Bayesian Belief Networks Is NP-Hard

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Computer and Information Sciences - ISCIS 2005 (ISCIS 2005)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 3733))

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Abstract

Exact inference problem in belief networks has been well studied in the literature and has various application areas. It is known that this problem and its approximation version are NP-hard. In this study, an alternative polynomial time transformation is provided from the well-known vertex cover problem. This new transformation may lead to new insights and polynomially solvable classes of the exact inference problem in Bayesian belief networks.

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References

  1. Batchelor, C.: Application of belief networks to water management studies. Agricultural Water Management 40, 51–57 (1999)

    Article  Google Scholar 

  2. Garey, M.R., Johnson, D.S.: A Guide to the Theory of NP-Completeness. W. H. Freeman and Company, New York (1979)

    MATH  Google Scholar 

  3. Pearl, J.: Fusion, propagation, and structuring in belief networks. Artificial Intelligence 29, 241–288 (1986)

    Article  MATH  MathSciNet  Google Scholar 

  4. Cooper, G.F.: The computational complexity of probabilistic inference using bayesian belief networks. Artificial Intelligence 42, 393–405 (1990)

    Article  MATH  MathSciNet  Google Scholar 

  5. Dagum, P., Luby, M.: Approximating probabilistic inference in bayesian belief networks is np-hard. Artificial Intelligence 60, 141–153 (1993)

    Article  MATH  MathSciNet  Google Scholar 

  6. Dagum, P., Luby, M.: An optimal approximation algorithm for bayesian inference. Artificial Intelligence 93, 1–27 (1997)

    Article  MATH  MathSciNet  Google Scholar 

  7. Roth, D.: On the hardness of approximate reasoning. Artificial Intelligence 82, 273–302 (1996)

    Article  MathSciNet  Google Scholar 

  8. Shachter, R.D.: Probabilistic inference and influence diagram. Operations Research 36, 586–602 (1988)

    Article  Google Scholar 

  9. Vazirani, V.: Approximation Algorithms. Springer, Berlin (2001)

    Google Scholar 

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Author information

Authors and Affiliations

  1. Faculty of Engineering and Natural Sciences, Sabancı University, 34956, Orhanli, Istanbul, Turkey

    Mustafa Tacettin & Tonguç Ünlüyurt

Authors
  1. Mustafa Tacettin
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  2. Tonguç Ünlüyurt
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Editor information

Editors and Affiliations

  1. Department of Computer Engineering, Boğaziçi University, 34342, Bebek, Istanbul, Turkey

    pInar Yolum  & Can Özturan  & 

  2. Computer Engineering Department, Boğaziçi University, 34342, Bebek, İstanbul, Turkey

    Tunga Güngör

  3. Computer Engineering Department, Bogazici University, 80815, Bebek, Istanbul, Turkey

    Fikret Gürgen

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© 2005 Springer-Verlag Berlin Heidelberg

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Tacettin, M., Ünlüyurt, T. (2005). An Alternative Proof That Exact Inference Problem in Bayesian Belief Networks Is NP-Hard. In: Yolum, p., Güngör, T., Gürgen, F., Özturan, C. (eds) Computer and Information Sciences - ISCIS 2005. ISCIS 2005. Lecture Notes in Computer Science, vol 3733. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11569596_96

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  • DOI: https://doi.org/10.1007/11569596_96

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-29414-6

  • Online ISBN: 978-3-540-32085-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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