Probability & Stochastic Processes

This user friendly resource will help you grasp the concepts of probability and stochastic processes, so you can apply them in professional engineering practice. The book presents concepts clearly as a sequence of building blocks that are identified either as an axiom, definition, or theorem. This approach provides a better understanding of the material, which can be used to solve practical problems. 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. 

 Features of this Text. 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.