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Probability, Statistical Optics, and Data Testing pp 264–293Cite as

Estimating a Probability Law

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Part of the book series: Springer Series in Information Sciences ((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 can be assumed while making the estimate. These guiding philosophies are discussed more generally in Chap. 16.

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Chapter 10

  1. E. T. Jaynes: IEEE Trans. SSC-4,227 (1968)

    Google Scholar 

  2. E. L. Kosarev: Comput. Phys. Commun. 20,69 (1980)

    Article  ADS  Google Scholar 

  3. J. MacQueen, J. Marschak: Proc. Natl. Acad. Sci. USA 72,3819 (1975)

    Article  MathSciNet  ADS  MATH  Google Scholar 

  4. R. Kikuchi, B. H. Soffer: J. Opt. Soc. Am. 67,1656 (1977)

    Article  ADS  Google Scholar 

  5. J. P. Burg: “Maximum entropy spectral analysis,” 37th Annual Soc. Exploration Geophysicists Meeting, Oklahoma City, 1967

    Google Scholar 

  6. S. J. Wernecke, L. R. D’Addario: IEEE Trans. C-26,351 (1977)

    Article  MATH  Google Scholar 

  7. S. Goldman: Information Theory (Prentice-Hall, New York 1953)

    MATH  Google Scholar 

  8. D. Marcuse: Engineering Quantum Electrodynamics (Harcourt, Brace and World, New York 1970)

    Google Scholar 

Additional Reading

  • Baierlein, R.: Atoms and Information Theory (Freeman, San Francisco 1971)

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  • Boyd, D. W., J. M. Steele: Ann. Statist. 6,932 (1978)

    Article  MathSciNet  MATH  Google Scholar 

  • Brillouin, L.: Science and Information Theory (Academic, New York 1962)

    MATH  Google Scholar 

  • Frieden, B. R.: Comp. Graph. Image Proc. 12,40 (1980)

    Article  Google Scholar 

  • Kullbach, S.: Information Theory and Statistics (Wiley, New York 1959)

    Google Scholar 

  • Sklansky, J., G. N. Wassel: Pattern Classifiers and Trainable Machines (Springer, Berlin, Heidelberg, New York 1981)

    Book  MATH  Google Scholar 

  • Tapia, R. A., J. R. Thompson: Nonparametric Probability Density Estimation (Johns Hopkins University Press, Baltimore 1978)

    MATH  Google Scholar 

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Author information

Authors and Affiliations

  1. Optical Sciences Center, The University of Arizona, 85721, Tucson, AZ, USA

    Professor B. Roy Frieden Ph. D.

Authors
  1. Professor B. Roy Frieden Ph. D.
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© 1983 Springer-Verlag Berlin Heidelberg

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Frieden, B.R. (1983). Estimating a Probability Law. In: Probability, Statistical Optics, and Data Testing. Springer Series in Information Sciences, vol 10. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-96732-0_10

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  • DOI: https://doi.org/10.1007/978-3-642-96732-0_10

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-96734-4

  • Online ISBN: 978-3-642-96732-0

  • eBook Packages: Springer Book Archive

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