With Bayesian techniques, programs can be written to select, among possible choices, the model which best explains the data. The program therefore makes inferences. Applications of Bayesian inference can be seen in pattern/image recognition, radar target identification, medical diagnosis, relevant-gene scoring, text/e-mail classification, and so on. Inclusion of Bayes theorem in neural networks also enhances artificial intelligence’s capability in such a way that it deals better with a real world full of errors and uncertainties.


Posterior Probability Bayesian Analysis Pixel Size Maximum Entropy Optical Character Recognition 
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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  1. The Pixon algorithm was introduced in, R.K. Pina and R.C. Puetter, “Bayesian Image Reconstruction: The Pixon and Optimal Image Modeling”, Publication of the Astronomical Society of the Pacific, 105 (1993) 630–637CrossRefGoogle Scholar
  2. The entropy was defined in, for the study of efficient communications, C.E. Shannon, “A Mathematical Theory of Communication”, Bell System Technical Journal, 27 (1948) 379–423MathSciNetzbMATHGoogle Scholar
  3. An introduction to Bayesian data analysis can be found in, G.L. Bretthorst, “Bayesian Spectrum Analysis and Parameter Estimation” in Lecture Notes in Statistics 48, Springer-Verlag, New York (1988)Google Scholar
  4. An online clearinghouse for the Bayesian approach to statistical inference is,, where enormous Bayes resources, such as naive Bayesian learning and belief network, can be reached.
  5. The following web site is dedicated to applications of entropy in such fields as information and coding theory, dynamical systems, logic and the theory of algorithms, statistical inference, and biology:

Copyright information

© Springer Science+Business Media New York 2003

Authors and Affiliations

  • Sun-Chong Wang
    • 1
  1. 1.TRIUMFCanada

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