Secure CheckoutPersonal information is secured with SSL technology.
Free ShippingFree global shipping
No minimum order.
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
Upper level undergraduate students and graduate level students
Preface to the Second Edition
Chapter 1. Some Motivating Examples
Chapter 2. Some Fundamental Concepts
2.1 Some Fundamental Concepts
2.2 Some Fundamental Results
2.3 Random Variables
2.4 Basic Concepts and Results in Counting
Chapter 3. The Concept of Probability and Basic Results
3.1 Definition of Probability
3.2 Some Basic Properties and Results
3.3 Distribution of a Random Variable
Chapter 4. Conditional Probability and Independence
4.1 Conditional Probability and Related Results
4.2 Independent Events and Related Results
Chapter 5. Numerical Characteristics of a Random Variable
5.1 Expectation, Variance, and Moment-Generating Function of a Random Variable
5.2 Some Probability Inequalities
5.3 Median and Mode of a Random Variable
Chapter 6. Some Special Distributions
6.1 Some Special Discrete Distributions
6.2 Some Special Continuous Distributions
Chapter 7. Joint Probability Density Function of Two Random Variables and Related Quantities
7.1 Joint d.f. and Joint p.d.f. of Two Random Variables
7.2 Marginal and Conditional p.d.f.’s, Conditional Expectation, and Variance
Chapter 8. Joint Moment-Generating Function, Covariance, and Correlation Coefficient of Two Random Variables
8.1 The Joint m.g.f. of Two Random Variables
8.2 Covariance and Correlation Coefficient of Two Random Variables
8.3 Proof of Theorem 1, Some Further Results
Chapter 9. Some Generalizations to k Random Variables, and Three Multivariate Distributions
9.1 Joint Distribution of k Random Variables and Related Quantities
9.2 Multinomial Distribution
9.3 Bivariate Normal Distribution
9.4 Multivariate Normal Distribution
Chapter 10. Independence of Random Variables and Some Applications
10.1 Independence of Random Variables and Criteria of Independence
10.2 The Reproductive Property of Certain Distributions
10.3 Distribution of the Sample Variance under Normality
Chapter 11. Transformation of Random Variables
11.1 Transforming a Single Random Variable
11.2 Transforming Two or More Random Variables
11.3 Linear Transformations
11.4 The Probability Integral Transform
11.5 Order Statistics
Chapter 12. Two Modes of Convergence, the Weak Law of Large Numbers, the Central Limit Theorem, and Further Results
12.1 Convergence in Distribution and in Probability
12.2 The Weak Law of Large Numbers and the Central Limit Theorem
12.3 Further Limit Theorems
Chapter 13. An Overview of Statistical Inference
13.1 The Basics of Point Estimation
13.2 The Basics of Interval Estimation
13.3 The Basics of Testing Hypotheses
13.4 The Basics of Regression Analysis
13.5 The Basics of Analysis of Variance
13.6 The Basics of Nonparametric Inference
Appendix 20. Appendix
Chapter 21. Some Notation and Abbreviations
Appendix 22. Answers to Even-Numbered Exercises
Appendix 23. Revised Answers Manual to Introduction to Probability
- No. of pages:
- © Academic Press 2014
- 2nd December 2013
- Academic Press
- Hardcover ISBN:
- eBook ISBN:
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.
University of California, Davis, USA
"...a very traditional mathematics text on the topic of probability. Readers should be comfortable with multiple integrals and, in spots, a little linear algebra. The writing is clear and concise." --MAA.org, August 18 2014
Elsevier.com visitor survey
We are always looking for ways to improve customer experience on Elsevier.com.
We would like to ask you for a moment of your time to fill in a short questionnaire, at the end of your visit.
If you decide to participate, a new browser tab will open so you can complete the survey after you have completed your visit to this website.
Thanks in advance for your time.