Fourier Analysis in Probability Theory
eBook - PDF

Fourier Analysis in Probability Theory

  1. 680 pages
  2. English
  3. PDF
  4. Only available on web
eBook - PDF

Fourier Analysis in Probability Theory

Book details
Table of contents
Citations

About This Book

Fourier Analysis in Probability Theory provides useful results from the theories of Fourier series, Fourier transforms, Laplace transforms, and other related studies. This 14-chapter work highlights the clarification of the interactions and analogies among these theories. Chapters 1 to 8 present the elements of classical Fourier analysis, in the context of their applications to probability theory. Chapters 9 to 14 are devoted to basic results from the theory of characteristic functions of probability distributors, the convergence of distribution functions in terms of characteristic functions, and series of independent random variables. This book will be of value to mathematicians, engineers, teachers, and students.

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Yes, you can access Fourier Analysis in Probability Theory by Tatsuo Kawata, Z. W. Birnbaum,E. Lukacs in PDF and/or ePUB format, as well as other popular books in Mathematics & Applied Mathematics. We have over one million books available in our catalogue for you to explore.

Information

Year
2014
ISBN
9781483218526

Table of contents

  1. Front Cover
  2. Fourier Analysis in Probability Theory
  3. Copyright Page
  4. Table of Contents
  5. Preface
  6. Chapter I. Introduction
  7. Chapter Il. Fourier Series and Fourier Transforms
  8. Chapter Ill. Fourierā€“Stieltjes Coefficients, Fourierā€“Stieltjes Transforms and Characteristic Functions
  9. Chapter IV. Convergence and Summability Theorems
  10. Chapter V. General Convergence Theorems
  11. Chapter VI. L2-Theory of Fourier Series and Fourier Transforms
  12. Chapter VII. Laplace and Mellin Transforms
  13. Chapter VIII. More Theorems on Fourier and Laplace Transforms
  14. Chapter IX. Convergence of Distribution Functions and Characteristic Functions
  15. Chapter X. Some Properties of Characteristic Functions
  16. Chapter XI. Distribution Functions and Their Characteristic Functions
  17. Chapter XII. Convergence of Series of Independent Random Variables
  18. Chapter XIII. Properties of Sums of Independent Random Variables; Convergence of Series in the Mean
  19. Chapter XIV. Some Special Series of Random Variables
  20. References
  21. Index