The Laws of Large Numbers
eBook - PDF

The Laws of Large Numbers

  1. 176 pages
  2. English
  3. PDF
  4. Only available on web
eBook - PDF

The Laws of Large Numbers

Book details
Table of contents
Citations

About This Book

The Law of Large Numbers deals with three types of law of large numbers according to the following convergences: stochastic, mean, and convergence with probability 1. The book also investigates the rate of convergence and the laws of the iterated logarithm. It reviews measure theory, probability theory, stochastic processes, ergodic theory, orthogonal series, Huber spaces, Banach spaces, as well as the special concepts and general theorems of the laws of large numbers. The text discusses the laws of large numbers of different classes of stochastic processes, such as independent random variables, orthogonal random variables, stationary sequences, symmetrically dependent random variables and their generalizations, and also Markov chains. It presents other laws of large numbers for subsequences of sequences of random variables, including some general laws of large numbers which are not related to any concrete class of stochastic processes. The text cites applications of the theorems, as in numbers theory, statistics, and information theory. The text is suitable for mathematicians, economists, scientists, statisticians, or researchers involved with the probability and relative frequency of large numbers.

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Yes, you can access The Laws of Large Numbers by Pál Révész, Z. W. Birnbaum,E. Lukacs in PDF and/or ePUB format, as well as other popular books in Matemáticas & Matemáticas general. We have over one million books available in our catalogue for you to explore.

Information

Publisher
Academic Press
Year
2014
ISBN
9781483269023
Topic
Matemáticas
Subtopic
Matemáticas general

Table of contents

  1. Front Cover
  2. The Laws of Large Numbers
  3. Copyright Page
  4. Table of Contents
  5. INTRODUCTION
  6. CHAPTER 0. MATHEMATICAL BACKGROUND
  7. CHAPTER 1. DEFINITIONS AND GENERALITIES
  8. CHAPTER 2. INDEPENDENT RANDOM VARIABLES
  9. CHAPTER 3. ORTHOGONAL RANDOM VARIABLES
  10. CHAPTER 4. STATIONARY SEQUENCES
  11. CHAPTER 5. SUBSEQUENCES OF SEQUENCES OF RANDOM VARIABLES
  12. CHAPTER 6. SYMMETRICALLY DEPENDENT RANDOM VARIABLES AND THEIR GENERALIZATIONS
  13. CHAPTER 7. MARKOV CHAINS
  14. CHAPTER 8. WEAKLY DEPENDENT RANDOM VARIABLES
  15. CHAPTER 9. INDEPENDENT RANDOM VARIABLES TAKING VALUES IN AN ABSTRACT SPACE
  16. CHAPTER 10. SUM OF A RANDOM NUMBER OF INDEPENDENT RANDOM VARIABLES
  17. CHAPTER 11. APPLICATIONS
  18. REFERENCES
  19. AUTHOR INDEX