Introduction to Probability

Other | September 1, 2006

byRoussas, George G., George G. Roussas

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Roussas'sIntroduction to Probabilityfeatures exceptionally clear explanations of the mathematics of probability theory and explores its diverse applications through numerous interesting and motivational examples. It provides a thorough introduction to the subject for professionals and advanced students taking their first course in probability. The content is based on the introductory chapters of Roussas's book,An Intoduction to Probability and Statistical Inference, with additional chapters and revisions.

• Written by a well-respected author known for great exposition and readability
• Boasts many real world examples
• Pedagogy includes chapter summaries, tables of distributions and formulas, and answers to even-numbered exercises

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From the Publisher

Roussas'sIntroduction to Probabilityfeatures exceptionally clear explanations of the mathematics of probability theory and explores its diverse applications through numerous interesting and motivational examples. It provides a thorough introduction to the subject for professionals and advanced students taking their first course in prob...

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-au...

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Format:OtherDimensions:400 pages, 1 × 1 × 1 inPublished:September 1, 2006Publisher:Academic PressLanguage:English

The following ISBNs are associated with this title:

ISBN - 10:0080509339

ISBN - 13:9780080509334

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Extra Content

Table of Contents

1. Some Motivating Examples
2. Some Fundamental Concepts
3. The Concept of Probability and Basic Results
4. Conditional Probability and Independence
5. Numerical Characteristics of a Random Variable
6. Some Special Distributions
7. 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
Appendix
Tables
Some Notation and Abbreviations
Answers to the Even-numbered Exercises