A Methodological Approach for the Effective Modeling of Bayesian Networks

  • Martin Atzmueller
  • Florian Lemmerich
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5243)


Modeling Bayesian networks manually is often a tedious task. This paper presents a methodological view onto the effective modeling of Bayesian networks. It features intuitive techniques that are especially suited for inexperienced users: We propose a process model for the modeling task, and discuss strategies for acquiring the network structure. Furthermore, we describe techniques for a simplified construction of the conditional probability tables using constraints and a novel extension of the Ranked-Nodes approach. The effectiveness and benefit of the presented approach is demonstrated by three case studies.


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  1. 1.
    Russell, S., Norvig, S.: Artificial Intelligence: A Modern Approach, 2nd edn. Prentice–Hall, Englewood Cliffs (2003)Google Scholar
  2. 2.
    Wrobel, S.: An Algorithm for Multi-Relational Discovery of Subgroups. In: Proc. 1st Europ. Symp. Principles of Data Mining and Knowledge Discovery, pp. 78–87. Springer, Berlin (1997)Google Scholar
  3. 3.
    Puppe, F.: Knowledge Reuse among Diagnostic Problem-Solving Methods in the Shell-Kit D3. Intl. Journal of Human-Computer Studies 49, 627–649 (1998)CrossRefGoogle Scholar
  4. 4.
    van der Gaag, L.C., Helsper, E.M.: Experiences with Modelling Issues in Building Probabilistic Networks. In: Gómez-Pérez, A., Benjamins, V.R. (eds.) EKAW 2002. LNCS (LNAI), vol. 2473, pp. 21–26. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  5. 5.
    Koller, D., Pfeffer, A.: Object–Oriented Bayesian Networks. In: Proceedings of the Thirteenth Conference on Uncertainty in Artificial Intelligence (UAI 1997), pp. 302–313 (1997)Google Scholar
  6. 6.
    Neil, M., Fenton, N., Nielsen, L.: Building Large-Scale Bayesian Networks. Knowledge Engineering Review (1999)Google Scholar
  7. 7.
    Fenton, N., Neil, M.: Ranked Nodes: A Simple and Effective Way to Model Qualitative Judgements in Large–Scale Bayesian Nets. IEEE Transactions on Knowledge and Data Engineering (2005)Google Scholar
  8. 8.
    Lucas, P.: Bayesian Network Modelling through Qualitative Patterns. Artificial Intelligence 163(2), 233–263 (2005)MATHCrossRefMathSciNetGoogle Scholar
  9. 9.
    Helsper, E., van der Gaag, L., Groenendaal, F.: Designing a Procedure for the Acquisition of Probability Constraints for Bayesian Networks. In: Motta, E., Shadbolt, N.R., Stutt, A., Gibbins, N. (eds.) EKAW 2004. LNCS (LNAI), vol. 3257, pp. 280–292. Springer, Heidelberg (2004)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Martin Atzmueller
    • 1
  • Florian Lemmerich
    • 1
  1. 1.Department of Computer Science VI Am HublandUniversity of WürzburgWürzburgGermany

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