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Bayesian Networks: Representation and Inference

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

Abstract

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.

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Copyright information

© Springer-Verlag London 2015

Authors and Affiliations

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

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