Maintaining their highly popular, user-friendly approach, Roy Yates and David Goodman demystify probability unlike any other text today. The authors help you develop an intuitive grasp of the principles of probability and stochastic processes, allowing you to successfully solve basic engineering problems using these principles... with a smile.

The authors present the principles of probability and stochastic processes as a logical sequence of building blocks that are clearly identified as an axiom, definition, or theorem. For each new principle, examples illustrate the application of the mathematics to engineering problems. You’ll also have many opportunities for practice.

Now revised, this Second Edition features a new chapter on random vectors, expanded coverage of the applications of probability, a streamlined presentation of Markov chains and elementary queuing theory, and a tutorial in each chapter on using MATLAB.

With this text, you’ll be able to:

• Develop an intuitive understanding of the concepts.
• Follow a single clear model that begins with an experiment consisting of a procedure and observations.
• Put the theory into practice with an extensive collection of exercises (examples, quizzes, and homework problems).
• Gain hands-on experience with MATLAB applications.
• Master basic principles without the confusion of learning two different ways of calculating probabilities and averages at the same time. (Discrete and continuous random variables are treated separately.)

Key Features:

• The text follows a single model that begins with an experiment consisting of a procedure and observations.
• The mathematics of discrete random variables appears separately from the mathematics of continuous random variables.
• Stochastic processes are introduced in Chapter 6, immediately after the presentation of discrete and continuous random variables. Subsequent material, including central limit theorem approximations, laws of large numbers, and statistical inference, then use examples that reinforce stochastic process concepts.
• An abundance of exercises are provided that help students learn how to put the theory to use.

Preface.

1. Experiments, Models, and Probabilities.

2. Discrete Random Variables.

3. Continuous Random Variables.

4. Pairs of Random Variables.

5. Random Vectors.

6. Sums of Random Variables.

7. Parameter Estimation Using the Sample Mean.

8. Hypothesis Testing.

9. Estimation of a Random Variable.

10. Stochastic Processes.

11. Random Signal Processing.

12. Markov Chains.

Appendix A: Families of Random Variables.

Appendix B: A Few Math Facts.

References.

Index.