This book presents elementary probability theory with interesting and well-chosen applications that illustrate the theory. An introductory chapter reviews the basic elements of differential calculus which are used in the material to follow.

The main intended audience for this book is undergraduate students in pure and applied sciences, especially those in engineering. Chapters 2 to 4 cover the probability theory they generally need in their training. Although the treatment of the subject is surely su?cient for non-mathematicians, I intentionally avoided getting too much into detail. For instance, topics such as mixed type random variables and the Dirac delta function are only brie?y mentioned. Courses on probability theory are often considered di?cult. However, after having taught this subject for many years, I have come to the conclusion that one of the biggest problems that the students face when they try to learn probability theory, particularly nowadays, is their de?ciencies in basic di?erential and integral calculus. Integration by parts, for example, is often already forgotten by the students when they take a course on probability. For this reason, I have decided to write a chapter reviewing the basic elements of di?erential calculus. Even though this chapter might not be covered in class, the students can refer to it when needed. In this chapter, an e?ort was made to give the readers a good idea of the use in probability theory of the concepts they should already know. Chapter 2 presents the main results of what is known as elementary probability, including Bayes' rule and elements of combinatorial analysis.

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Inhalt
Preface.- Review of Differential Calculus.- Elementary Probability.- Random Variables.- Random Vectors.- Reliability.- Queueing.- Time Series.- Appendix A: List of Symbols and Abbreviations.- Appendix B: Statistical Tables.- Appendix C: Solutions to 'Solved Exercises'.- Appendix D: Answers to Even-Numbered Exercises.- Appendix E: Answers to Multiple-Choice Questions.- References.- Index.