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Estimating a Probability Law

  • B. Roy Frieden
Part of the Springer Series in Information Sciences book series (SSINF, volume 10)

Abstract

Estimating the probability law that gave rise to given data is one of the chief aims of statistics. Once known, its variance, confidence limits, and all other parameters describing fluctuation may be determined. There are two main schools of thought — the classical and the Bayesian — regarding what may be assumed while making the estimate. These guiding philosophies are discussed more generally in Chap. 16.

Keywords

Prior Knowledge Maximum Entropy Laser Speckle Resolution Cell Orthogonal Expansion 
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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References

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Additional Reading

  1. Baierlein, R.: Atoms and Information Theory (Freeman, San Francisco 1971)Google Scholar
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  4. Frieden, B. R.: Comp. Graph. Image Proc. 12, 40 (1980)CrossRefGoogle Scholar
  5. Kullbach, S.: Information Theory and Statistics (Wiley, New York 1959)Google Scholar
  6. Sklansky, J., G. N. Wassel: Pattern Classifiers and Trainable Machines (Springer, Berlin, Heidelberg, New York 1981)MATHCrossRefGoogle Scholar
  7. Tapia, R. A., J. R. Thompson: Nonparametric Probability Density Estimation (Johns Hopkins University Press, Baltimore 1978)MATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1991

Authors and Affiliations

  • B. Roy Frieden
    • 1
  1. 1.Optical Sciences CenterThe University of ArizonaTucsonUSA

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