Bayesian Networks: Representation and Inference

  • Luis Enrique SucarEmail author
Part of the Advances in Computer Vision and Pattern Recognition book series (ACVPR)


This chapter introduces Bayesian networks, covering representation and inference. The basic representational aspects of a Bayesian network are presented, including the concept of D-Separation and the independence axioms. With respect to parameter specification, the two main alternatives for a compact representation are described, one based on canonical models and the other on graphical representations. Then the main algorithms for probabilistic inference are introduced, including belief propagation, variable elimination, conditioning, junction trees, loopy propagation, and stochastic simulation. The chapter concludes by illustrating the application of Bayesian networks in information validation and system reliability analysis.


  1. 1.
    Cooper, G.F.: The computational complexity of probabilistic inference using Bayesian networks. Artif. Intell. 42, 393–405 (1990)zbMATHCrossRefGoogle Scholar
  2. 2.
    Darwiche, A.: Modeling and Reasoning with Bayesian Networks. Cambridge University Press, New York (2009)zbMATHCrossRefGoogle Scholar
  3. 3.
    Díez, F.J., Druzdzel, M.J.: Canonical probabilistic models for knowledge engineering. Technical Report CISIAD-06-01. Universidad Nacional de Educación a Distancia, Spain (2007)Google Scholar
  4. 4.
    Ibargüengoytia, P.H., Sucar, L.E., Vadera, S.: A probabilistic model for sensor validation. In: Proceedings of the Twelfth Conference on Uncertainty in Artificial Intelligence UAI-96, pp. 332–339. Morgan Kaufmann Publishers Inc. (1996)Google Scholar
  5. 5.
    Ibargüengoytia, P.H., Vadera, S., Sucar, L.E.: A probabilistic model for information validation. Br. Comput. J. 49(1), 113–126 (2006)CrossRefGoogle Scholar
  6. 6.
    Jensen, F.V., Andersen, S.K.: Approximations in Bayesian belief universes for knowledge based systems. In: Proceedings of the Sixth Conference on Uncertainty in Artificial Intelligence UAI-90, pp. 162–169. Elsevier, New York (1990)Google Scholar
  7. 7.
    Jensen, F.V.: Bayesian Networks and Decision Graphs. Springer, New York (2001)zbMATHCrossRefGoogle Scholar
  8. 8.
    Korb, K.B., Nicholson, A.E.: Bayesian Artificial Intelligence, 2nd edn. CRC Press, Boca Raton (2010)Google Scholar
  9. 9.
    Lauritzen, S., Spiegelhalter, D.J.: Local computations with probabilities on graphical structures and their application to expert systems. J. R. Stat. Soc. Ser. B. 50(2), 157–224 (1988)zbMATHMathSciNetGoogle Scholar
  10. 10.
    Moral, S., Rumi, R., Salmerón, A.: Mixtures of truncated exponentials in hybrid Bayesian networks. Symb. Quant. Approaches Reason. Uncertain. 2143, 156–167 (2001)CrossRefGoogle Scholar
  11. 11.
    Murphy, K.P., Weiss, Y., Jordan, M.: Loopy belief propagation for approximate inference: an empirical study. In: Proceedings of the Fifteenth Conference on Uncertainty in Artificial Intelligence, pp. 467–475. Morgan Kaufmann Publishers Inc. (1999)Google Scholar
  12. 12.
    Neapolitan, R.E.: Probabilistic Reasoning in Expert Systems. Wiley, New York (1990)Google Scholar
  13. 13.
    Pearl, J.: Fusion, propagation and structuring in belief networks. Artif. Intell. 29, 241–288 (1986)zbMATHMathSciNetCrossRefGoogle Scholar
  14. 14.
    Pearl, J.: Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. Morgan Kaufmann, San Francisco (1988)Google Scholar
  15. 15.
    Pourret, O., Naim, P., Marcot, B. (eds.): Bayesian Belief Networks: A Practical Guide to Applications. Wiley, New Jersey (2008)Google Scholar
  16. 16.
    Shenoy, P., Shafer, G.: Axioms for probability and belief-function propagation. Uncertainty in Artificial Intelligence, vol. 4, pp. 169–198. Elsevier, New York (1990)Google Scholar
  17. 17.
    Torres-Toledano, J.G., Sucar, L.E.: Bayesian networks for reliability analysis of complex systems. In: Coelho, H. (ed.) IBERAMIA’98. Lecture Notes in Computer Science, vol. 1484, pp. 195–206. Springer, Berlin (1998)Google Scholar

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© Springer-Verlag London 2015

Authors and Affiliations

  1. 1.Instituto Nacional de Astrofísica, Óptica y Electrónica (INAOE)Santa María TonantzintlaMexico

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