Bayesian Networks: Learning

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


This chapter gives an introduction to learning Bayesian networks including both parameter and structure learning. Parameter learning includes how to handle uncertainty in the parameters and missing data; it also includes the basic discretization techniques. After describing the techniques for learning tree and polytree BNs, the two main types of methods for structure learning are described: score and search, and independence tests. We then describe how to combine expert knowledge and data. The chapter concludes with an application example in the area of pollution modeling.


Mutual Information Bayesian Network Hide Node Minimum Description Length Independence Test 
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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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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