Introduction to Probability
2nd Edition
Published on 27. November 2013
546 pages
978-0-12-800198-1 (ISBN)
Description
Introduction to Probability, Second Edition, discusses probability theory in a mathematically rigorous, yet accessible way. This one-semester basic probability textbook explains important concepts of probability while providing useful exercises and examples of real world applications for students to consider.
This edition demonstrates the applicability of probability to many human activities with examples and illustrations. After introducing fundamental probability concepts, the book proceeds to topics including conditional probability and independence; numerical characteristics of a random variable; special distributions; joint probability density function of two random variables and related quantities; joint moment generating function, covariance and correlation coefficient of two random variables; transformation of random variables; the Weak Law of Large Numbers; the Central Limit Theorem; and statistical inference. Each section provides relevant proofs, followed by exercises and useful hints. Answers to even-numbered exercises are given and detailed answers to all exercises are available to instructors on the book companion site.
This book will be of interest to upper level undergraduate students and graduate level students in statistics, mathematics, engineering, computer science, operations research, actuarial science, biological sciences, economics, physics, and some of the social sciences.
- Demonstrates the applicability of probability to many human activities with examples and illustrations
- Discusses probability theory in a mathematically rigorous, yet accessible way
- Each section provides relevant proofs, and is followed by exercises and useful hints
- Answers to even-numbered exercises are provided and detailed answers to all exercises are available to instructors on the book companion site
More details
Other editions
Additional editions
George G. Roussas
Introduction to Probability
Book
01/2014
2nd Edition
Academic Press
€107.69
Shipment within 15-20 days
Previous edition
George G. Roussas
Introduction to Probability
E-Book
09/2006
1st Edition
Academic Press
€58.99
Available for download
Person
George G. Roussas earned a B.S. in Mathematics with honors from the University of Athens, Greece, and a Ph.D. in Statistics from the University of California, Berkeley. As of July 2014, he is a Distinguished Professor Emeritus of Statistics at the University of California, Davis. Roussas is the author of five books, the author or co-author of five special volumes, and the author or co-author of dozens of research articles published in leading journals and special volumes. He is a Fellow of the following professional societies: The American Statistical Association (ASA), the Institute of Mathematical Statistics (IMS), The Royal Statistical Society (RSS), the American Association for the Advancement of Science (AAAS), and an Elected Member of the International Statistical Institute (ISI); also, he is a Corresponding Member of the Academy of Athens. Roussas was an associate editor of four journals since their inception, and is now a member of the Editorial Board of the journal Statistical Inference for Stochastic Processes. Throughout his career, Roussas served as Dean, Vice President for Academic Affairs, and Chancellor at two universities; also, he served as an Associate Dean at UC-Davis, helping to transform that institution's statistical unit into one of national and international renown. Roussas has been honored with a Festschrift, and he has given featured interviews for the Statistical Science and the Statistical Periscope. He has contributed an obituary to the IMS Bulletin for Professor-Academician David Blackwell of UC-Berkeley, and has been the coordinating editor of an extensive article of contributions for Professor Blackwell, which was published in the Notices of the American Mathematical Society and the Celebratio Mathematica.
Content
Preface1. Some Motivating Examples2. Some Fundamental Concepts 3. The Concept of Probability and Basic Results4. Conditional Probability and Independence5. Numerical Characteristics of a Random Variable 6. Some Special Distributions7. Joint Probability Density Function of Two Random Variables and Related Quantities 8. Joint Moment Generating Function, Covariance and Correlation Coefficient of Two Random Variables 9. Some Generalizations to k Random Variables, and Three Multivariate Distributions 10. Independence of Random Variables and Some Applications 11. Transformation of Random Variables 12. Two Modes of Convergence, the Weak Law of Large Numbers, the Central Limit Theorem, and Further Results 13. An Overview of Statistical Inference AppendixSome Notation and Abbreviations Answers to the Even-Numbered ExercisesIndex
