Introduction to Probability, by George G. Roussas.

# ISBN-13: 9780128000410 
# Publisher: Elsevier Science
# Publication date: 12/16/2013
# Edition number: 2
# Pages: 546


Introduction to Probability, Second Edition, is written for students in statistics, mathematics, engineering, computer science, operations research, actuarial science, biological sciences, economics, physics, and some of the social sciences with a background in a year-long elementary calculus, taking upper level or graduate level introduction to probability courses.

Roussas utilizes his trademark clarity and economy of expression to elucidate important concepts of probability, while providing a plethora of useful examples and exercises of real world applications for students to consider. 


Key Features
============

* Includes text, examples, and graphical illustrations-where appropriate-to
  motivate the reader, and also demonstrate the applicability of probability
  in a great variety of human activities

* Provides a mathematically relatively rigorous, yet accessible and always
  within the prescribed prerequisites, discussion of probability theory,
  important to students of all disciplines cited above

* Each section provides relevant proofs and is followed by exercises and
  hints, providing useful clues to the solutions

* Answers to even-numbered exercises are provided and detailed answers to
  all exercises are available to instructors on the book companion site
  
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 


Author
======
George G. Roussas is a Distinguished Professor Emeritus (as of July 01, 2012) of Statistics at the University of California, Davis (UC-Davis), and a well-known author of books, research monographs, editor/co-editor of special volumes, and author/co-author of dozens of research articles published in leading journals of the profession

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