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Bayesian Networks for Expert Systems: Theory and Practical Applications

  • Wim Wiegerinck
  • Bert Kappen
  • Willem Burgers
Part of the Studies in Computational Intelligence book series (SCI, volume 281)

Abstract

Bayesian networks are widely accepted as models for reasoning with uncertainty. In this chapter, we focus on models that are created using domain expertise only. After a short review of Bayesian network models and common Bayesian network modeling approaches, we will discuss in more detail three applications of Bayesian networks.With these applications, we aim to illustrate the modeling power and flexibility of the Bayesian networks, which go beyond the standard textbook applications. The first network is applied in a system for medical diagnostic decision support. A distinguishing feature of this network is the large amount of variables in the model. The second one involves an application for petrophysical decision support to determine the mineral content of a well, based on borehole measurements. This model differs from standard Bayesian networks in terms of its continuous variables and nonlinear relations. Finally, we will discuss an application for victim identification by kinship analysis based on DNA profiles. The distinguishing feature in this application is that Bayesian networks are generated and computed on-the-fly based on case information.

Keywords

Expert System Bayesian Network Observation Model Short Tandem Repeat Locus Bayesian Network Modeling 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    Balding, D., Nichols, R.: DNA profile match probability calculation: how to allow for population stratification, relatedness, database selection and single bands. Forensic Science International 64(2-3), 125–140 (1994)CrossRefGoogle Scholar
  2. 2.
    Beinlich, I., Suermondt, H., Chavez, R., Cooper, G., et al.: The ALARM monitoring system: A case study with two probabilistic inference techniques for belief networks. In: Proceedings of the Second European Conference on Artificial Intelligence in Medicine, vol. 256. Springer, Berlin (1989)Google Scholar
  3. 3.
    Bishop, C.: Pattern recognition and machine learning. Springer, Heidelberg (2006)zbMATHCrossRefGoogle Scholar
  4. 4.
    Brinkmann, B., Klintschar, M., Neuhuber, F., Hühne, J., Rolf, B.: Mutation rate in human microsatellites: influence of the structure and length of the tandem repeat. The American Journal of Human Genetics 62(6), 1408–1415 (1998)CrossRefGoogle Scholar
  5. 5.
    Burgers, W., Wiegerinck, W., Kappen, H., Spalburg, M.: A Bayesian petrophysical decision support system for estimation of reservoir compositions (submitted)Google Scholar
  6. 6.
    Butler, J.: Forensic DNA typing: biology, technology, and genetics of STR markers. Academic Press, London (2005)Google Scholar
  7. 7.
    Castillo, E., Gutierrez, J.M., Hadi, A.S.: Expert Systems and Probabilistic Network Models. Springer, Heidelberg (1997)Google Scholar
  8. 8.
    Dawid, A., Mortera, J., Pascali, V.: Non-fatherhood or mutation? A probabilistic approach to parental exclusion in paternity testing. Forensic science international 124(1), 55–61 (2001)CrossRefGoogle Scholar
  9. 9.
    Drábek, J.: Validation of software for calculating the likelihood ratio for parentage and kinship. Forensic Science International: Genetics 3(2), 112–118 (2009)CrossRefGoogle Scholar
  10. 10.
    Duane, S., Kennedy, A., Pendleton, B., Roweth, D.: Hybrid Monte Carlo Algorithm. Phys. Lett. B 195, 216 (1987)CrossRefGoogle Scholar
  11. 11.
    Fishelson, M., Geiger, D.: Exact genetic linkage computations for general pedigrees. Bioinformatics 198(Suppl. 1), S189–S198 (2002)Google Scholar
  12. 12.
    Friedman, N., Geiger, D., Lotner, N.: Likelihood computations using value abstraction. In: Proceedings of the Sixteenth Conference on Uncertainty in Artificial Intelligence, pp. 192–200. Morgan Kaufmann Publishers, San Francisco (2000)Google Scholar
  13. 13.
    Heckerman, D.: Probabilistic interpretations for mycin’s certainty factors. In: Kanal, L., Lemmer, J. (eds.) Uncertainty in artificial intelligence, pp. 167–196. North-Holland, Amsterdam (1986)Google Scholar
  14. 14.
    Jensen, F.: An Introduction to Bayesian networks. UCL Press (1996)Google Scholar
  15. 15.
    Jordan, M.: Learning in graphical models. Kluwer Academic Publishers, Dordrecht (1998)zbMATHGoogle Scholar
  16. 16.
    Lauritzen, S., Spiegelhalter, D.: Local computations with probabilities on graphical structures and their application to expert systems. Journal of the Royal Statistical Society. Series B (Methodological), 157–224 (1988)Google Scholar
  17. 17.
    MacKay, D.: Information theory, inference and learning algorithms. Cambridge University Press, Cambridge (2003)zbMATHGoogle Scholar
  18. 18.
    Mahoney, S., Laskey, K.: Network engineering for complex belief networks. In: Proc. 12th Conf. on Uncertainty in Artificial Intelligence, pp. 389–396. Morgan Kaufmann, San Francisco (1996)Google Scholar
  19. 19.
    Metropolis, N., Rosenbluth, A., Rosenbluth, M., Teller, A., Teller, E.: Equation of state calculations by fast computing machines. The journal of chemical physics 21(6), 1087 (1953)CrossRefGoogle Scholar
  20. 20.
    Pearl, J.: Probabilistic Reasoning in Intelligent systems: Networks of Plausible Inference. Morgan Kaufmann Publishers, Inc., San Francisco (1988)Google Scholar
  21. 21.
    Pradhan, M., Provan, G., Middleton, B., Henrion, M.: Knowledge engineering for large belief networks. In: Proc. Tenth Conf. on Uncertainty in Artificial Intelligence, pp. 484–490 (1994)Google Scholar
  22. 22.
    Russell, S., Norvig, P., Canny, J., Malik, J., Edwards, D.: Artificial intelligence: a modern approach. Prentice Hall, Englewood Cliffs (2003)Google Scholar
  23. 23.
    Schlumberger: Log Interpretation Principles/Applications. Schlumberger Limited (1991)Google Scholar
  24. 24.
    Shortliffe, E., Buchanan, B.: A model of inexact reasoning in medicine. Mathematical Biosciences 23(3-4), 351–379 (1975)CrossRefMathSciNetGoogle Scholar
  25. 25.
    Shwe, M., Middleton, B., Heckerman, D., Henrion, M., Lehman, H., Cooper, G.: Probabilistic Diagnosis Using a Reformulation of the Internist-1/ QMR Knowledge Base. Methods of Information in Medicine 30, 241–255 (1991)Google Scholar
  26. 26.
    Spalburg, M.: Bayesian uncertainty reduction for log evaluation. SPE International (2004); SPE88685Google Scholar
  27. 27.
    Takinawa, M., D’Ambrosio, B.: Multiplicative factorization of noisy-MAX. In: Proceedings of the 15th Conference on Uncertainty in Artificial Intelligence UAI 1999, pp. 622–630 (1999)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Wim Wiegerinck
    • 1
  • Bert Kappen
    • 2
  • Willem Burgers
    • 1
  1. 1.SNN Adaptive IntelligenceNijmegenThe Netherlands
  2. 2.Donders Institute for Brain, Cognition and BehaviourRadboud University NijmegenNijmegenThe Netherlands

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