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.
KeywordsPosterior Probability Bayesian Analysis Pixel Size Maximum Entropy Optical Character Recognition
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- 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
- An online clearinghouse for the Bayesian approach to statistical inference is, http://astrosun.tn.Cornell.edu/staff/loredo/bayes/, where enormous Bayes resources, such as naive Bayesian learning and belief network, can be reached.
- 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: http://www.informatik.unitrier.de/~damm/Lehre/InfoCode/entropy.html