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Probability Theory

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

Abstract

This chapter presents an overview of some basic concepts in probability theory which are important for understanding probabilistic graphical models. First, the main interpretations and mathematical definition of probability are introduced. Second, the basic rules of probability theory are presented, including the concept of conditional independence and Bayes rule. Third, an overview of random variables and some important distributions are described. Lastly, the basics of information theory are presented.

Keywords

Cumulative Distribution Function Product Rule Conditional Independence Joint Probability Distribution Discrete Random Variable 
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.

References

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    Gillies, D.: Philosophical Theories of Probability. Routledge, London (2000)Google Scholar
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    Jaynes, E.T.: Probability Theory: The Logic of Science. Cambridge University Press, Cambridge (2003)CrossRefGoogle Scholar
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    MacKay, D.J.: Information Theory, Inference and Learning Algorithms. Cambridge University Press, Cambridge (2004)Google Scholar
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    Sucar, L.E., Gillies, D.F., Gillies, D.A.: Objective Probabilities in Expert Systems. Artif. Intell. 61, 187–208 (1993)MathSciNetCrossRefGoogle Scholar
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    Wasserman, L.: All of Statistcs: A Concise Course in Statistical Inference. Springer, New York (2004)CrossRefGoogle 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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