A Graduate Course in Probability
eBook - PDF

A Graduate Course in Probability

  1. 288 pages
  2. English
  3. PDF
  4. Only available on web
eBook - PDF

A Graduate Course in Probability

Book details
Table of contents
Citations

About This Book

Probability and Mathematical Statistics: A Series of Monographs and Textbooks: A Graduate Course in Probability presents some of the basic theorems of analytic probability theory in a cohesive manner. This book discusses the probability spaces and distributions, stochastic independence, basic limiting operations, and strong limit theorems for independent random variables. The central limit theorem, conditional expectation and martingale theory, and Brownian motion are also elaborated. The prerequisite for this text is knowledge of real analysis or measure theory, particularly the Lebesgue dominated convergence theorem, Fubini's theorem, Radon-Nikodym theorem, Egorov's theorem, monotone convergence theorem, and theorem on unique extension of a sigma-finite measure from an algebra to the sigma-algebra generated by it. This publication is suitable for a one-year graduate course in probability given in a mathematics program and preferably for students in their second year of graduate work.

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Yes, you can access A Graduate Course in Probability by Howard G. Tucker, Z. W. Birnbaum,E. Lukacs in PDF and/or ePUB format, as well as other popular books in Mathematik & Wahrscheinlichkeitsrechnung & Statistiken. We have over one million books available in our catalogue for you to explore.

Information

Publisher
Academic Press
Year
2014
ISBN
9781483220505
Topic
Mathematik
Subtopic
Wahrscheinlichkeitsrechnung & Statistiken

Table of contents

  1. Front Cover
  2. A Graduate Course in Probability
  3. Copyright Page
  4. Table of Contents
  5. Dedication
  6. Preface
  7. CHAPTER 1. Probability Spaces
  8. CHAPTER 2. Probability Distributions
  9. CHAPTER 3. Stochastic Independence
  10. CHAPTER 4. Basic Limiting Operations
  11. CHAPTER 5. Strong Limit Theorems for Independent Random Variables
  12. CHAPTER 6. The Central Limit Theorem
  13. CHAPTER 7. Conditional Expectation and Martingale Theory
  14. CHAPTER 8. An Introduction to Stochastic Processes and, in Particular, Brownian Motion
  15. Suggested Reading
  16. Index