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Approaches to Bayesian Network Model Construction

  • Ifeyinwa E. Achumba
  • Djamel Azzi
  • Ifeanyi Ezebili
  • Sebastian Bersch
Chapter
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 229)

Abstract

Bayesian Network (BN) has sound mathematical basis, enables reasoning under uncertainty, and facilitates the update of beliefs, given new evidence. It also enables the visual representation of a model. These make BN suitable for solving uncertainty problems. This chapter details BN model construction approaches and presents our experiences with selecting the optimal construction approach.

Keywords

Bayesian networks BN model construction BN model parameterization Parameter learning Performance index Structure learning 

Notes

Acknowledgments

This work was supported in part by the Schlumberger Foundation under its Faculty For The Future (FFTF) scholarship programme aimed at encouraging women, in Science, Engineering and Technology (STE), in their pursuit for academic excellence.

References

  1. 1.
    Jenson FV (2001) Bayesian networks and decision graphs. Springer, New YorkGoogle Scholar
  2. 2.
    Collins V, Greer JE, Huang SX (1996) Adaptive assessment using granularity hierarchies and bayesian nets. Lect Notes Comput Sci 1086:569–577CrossRefGoogle Scholar
  3. 3.
    Achumba IE, Azzi D, Ezebili I, Bersch SD (2012) On selecting the optimal Bayesian network model construction approach. Lecture notes in engineering and computer science: proceedings of the world congress on engineering, WCE 2012, 4–6 July 2012 U.K , London, pp 690–695Google Scholar
  4. 4.
    Cowell RG (1999) Parameter learning from incomplete data for Bayesian networks, 1999. http://www.staff.city.ac.uk/~rgc/webpages/aistats99.pdf
  5. 5.
    Oteniya L () Bayesian belief networks for dementia diagnosis and other applications: a comparison of hand-crafting and construction using a novel data driven technique. Unpublished Ph.D. thesis, Department of Computing Science, University of Stirling, Stirling, FK9 4LA, ScotlandGoogle Scholar
  6. 6.
    Chickering DM, Geiger D, Heckerman D (1995) Learning Bayesian networks: search methods and experimental results, 1995. http://research.microsoft.com/en-us/um/people/dmax/publications/aistats95.pdf
  7. 7.
    Forster M, Sober E (2010) AIC scores as evidence—a Bayesian interpretation, 2010. http://philosophy.wisc.edu/sober/forster%20and%20sober%20AIC%20Scores%20as%20Evidence%20jan%2028%202010.pdf
  8. 8.
    Robinson RW (1973) Counting labelled acyclic digraphs. In: Harary F (ed) New directions in the theory of graphs. Academic Press, New York, pp 239–273Google Scholar
  9. 9.
    Heinrich G (2010) Parameter estimation for text analysis. Technical note version 2.4, vsonix GmbH and University of Leipzig, August, 2008. http://www.arbylon.net/publications/text-est.pdf. Accessed 9 July 2010
  10. 10.
    Fenton N, Neil M, Caballero JG (2006) Using ranked nodes to model qualitative judgements in Bayesian networks. IEEE Trans Knowl Data Eng 19(10):1420–1432CrossRefGoogle Scholar
  11. 11.
    QAA (Quality Assurance Agency), (2006). Code of practice Section 6: assessment of students. http://www.qaa.ac.uk/academicinfrastructure/codeOfPractice/section6/default.asp. Accessed 3 Feb 2008
  12. 12.
    Harvey L (2004) “Analytic quality glossary”, Quality Research International, 2004. http://www.qualityresearchinternational.com/glossary/
  13. 13.
    Morgan M, Henrion M (1990) Uncertainty: a guide to dealing with uncertainty in quantitative risk and policy analysis. Cambridge University Press, LondonCrossRefGoogle Scholar
  14. 14.
    Pennock D (2006) Evaluating probabilistic predictions. http://blog.oddhead.com/2006/12/26/evaluating-probabilistic-predictions/. Accessed Dec 2006
  15. 15.
    Doshi P, Greenwald L, Clarke J (2002) Towards effective structure learning for large Bayesian networks. AAAI technical report WS-02-14, 2002. http://www.aaai.org/Papers/Workshops/2002/WS-02-14/WS02-14-003.pdf
  16. 16.
    NSC (Norsys Software Corp), Netica-J Reference Manual (Version 3.25), 2008. http://www.norsys.com/netica-j/docs/NeticaJ_Man.pdf
  17. 17.
    Good IJ (1952) Rational decisions. J Royal Stat Soc 14:107–114. In: Roulston M, The logarithmic scoring rule a.k.a. “ignorance”. http://www.cawcr.gov.au/bmrc/wefor/staff/eee/verif/Ignorance.html
  18. 18.
    de Jongh M, Druzdzel MJ (2009) A comparison of structural distance measures for causal Bayesian network models. Recent advances in intelligent information systems, 2009, pp 443–456. http://iis.ipipan.waw.pl/2009/proceedings/iis09-43.pdf. Accessed 07 Sept 2010
  19. 19.
    Pearl J, Russell S (2010) Bayesian networks, November 2000. http://www.cs.berkeley.edu/~russell/papers/hbtnn-bn.ps. Accessed 12 Feb 2010
  20. 20.
    MR (Microsoft Research) (2011) WinMine Toolkit Tutorial. http://research.microsoft.com/en-us/um/people/dmax/WinMine/Tutorial/Tutorial.html. Accessed 02 Feb 2011

Copyright information

© Springer Science+Business Media Dordrecht 2013

Authors and Affiliations

  • Ifeyinwa E. Achumba
    • 1
  • Djamel Azzi
    • 1
  • Ifeanyi Ezebili
    • 2
  • Sebastian Bersch
    • 1
  1. 1.Faculty of Technology, School of EngineeringUniversity of PortsmouthPortsmouthUK
  2. 2.Department of Electrical and Electronic Engineering, School of Engineering and Engineering TechnologyFederal University of Technology, OwerriOwerriNigeria

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