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Fourier Methods in Probability

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Probability, Statistical Optics, and Data Testing

Part of the book series: Springer Series in Information Sciences ((SSINF,volume 10))

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Abstract

There is probably no aspect of probability theory that is easier to learn than its Fourier aspect. All of the linear theory [4.1] involving convolutions, Dirac delta functions, transfer theorems, and even sampling theorems has its counterparts in probability theory.

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Chapter 4

  1. R. M. Bracewell: The Fourier Transform and Its Applications (McGraw-Hill, New York 1965)

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Additional Reading

  • Araujo, A., E. Gine: The Central Limit Theorem for Real and Banach Valued Random Variables (Wiley, New York 1980)

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  • Lukacs, E.: Characteristic Functions, 2nd ed. (Griffin, London 1970)

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  • Moran, P. A. P.: An Introduction to Probability Theory (Clarendon, Oxford 1968)

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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). Fourier Methods in Probability. 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_4

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  • DOI: https://doi.org/10.1007/978-3-642-96732-0_4

  • 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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