This book is a revised and expanded edition of a successful graduate and reference text. The material in the book is designed for a standard graduate course on probability theory, including some important applications. This new edition contains a detailed treatment of the core area of probability, and both structural and limit results are presented in full detail. Compared to the first edition, the material and presentation are better highlighted with several (small and large) alterations made to each chapter. Key features of the book include:
- Indicating the need for abstract theory even in applications and showing the inadequacy of existing results for certain apparently simple real-world problems
- Attempting to deal with the existence problems for various classes of random families that figure in the main results of the subject
- Providing a treatment of conditional expectations and of conditional probabilities that is more complete than in other existing textbooks
Since this is a textbook, essentially all proofs are given in complete detail (even at the risk of repetition), and some key results are given multiple proofs when each argument has something to contribute.
Rezensionen / Stimmen
From the reviews of the second edition:
"This book is a thoroughly revised and updated version of the first edition written by M. M. Rao. It expresses the in many ways original views of the authors on how the subject could be presented in graduate courses for students of mathematical sciences. . Generally, lecturers will appreciate this book as a valuable source . . the reviewer will definitely make use of this book for developing and revising his own lectures and encourage serious students to use it . ." (Frank Beichelt, SIAM Review, Vol. 49 (2), 2007)
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ISBN-13
978-0-387-27731-8 (9780387277318)
DOI
Schweitzer Klassifikation
Foundations.- Background Material and Preliminaries.- Independence and Strong Convergence.- Conditioning and Some Dependence Classes.- Analytical Theory.- Probability Distributions and Characteristic Functions.- Weak Limit Laws.- Applications.- Stopping Times, Martingales, and Convergence.- Limit Laws for Some Dependent Sequences.- A Glimpse of Stochastic Processes.
