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Graph Theory

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

Abstract

In this chapter, a review of some aspects of graph theory that are important for probabilistic graphical models are presented. After providing a definition of directed and undirected graphs, some basic theoretical graph concepts are introduced, including types of graphs, trajectories and circuits, and graph isomorphism. A section is dedicated to trees, an important type of graph. Some more advanced theoretical graph aspects required for inference in probabilistic models are introduced, including cliques, triangulated graphs, and perfect orderings. The chapter concludes with a description of the maximum cardinality search and graph filling algorithms.

References

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    Gross, J.L., Yellen, J.: Graph Theory and its Applications. CRC Press, Boca Raton (2005)Google Scholar
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    Neapolitan, R.: Probabilistic Reasoning in Expert Systems: Theory and Algorithms. Wiley, New York (1990)Google Scholar

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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