Intuitive probability and random processes using MATLAB

Serving as the foundation for a one-semester course in stochastic processes for students familiar with elementary probability theory and calculus, Introduction to Stochastic Modeling, Third Edition, bridges the gap between basic probability and an intermediate level course in stochastic processes. The objectives of the text are to introduce students to the standard concepts and methods of stochastic modeling, to illustrate the rich diversity of applications of stochastic processes in the applied sciences, and to provide exercises in the application of simple stochastic analysis to realistic problems.* Realistic applications from a variety of disciplines integrated throughout the text* Plentiful, updated and more rigorous problems, including computer "challenges"* Revised end-of-chapter exercises sets-in all, 250 exercises with answers* New chapter on Brownian motion and related processes* Additional sections on Matingales and Poisson process* Solutions manual available to adopting instructors "This book is an introduction to probability and random processes that merges theory with practice. Based on the author's belief that only "hands on" experience with the material can promote intuitive understanding the approach is to motivate the need for theory using MATLAB examples, followed by theory and analysis, and finally descriptions of "real-world" examples to acquaint the reader with a wide variety of applications."--Jacket. Introduction.- Computer Simulation.- Basic Probability.- Conditional Probability.- Discrete Random Variables.- Expected Values for Discrete Random Variables.- Multiple Discrete Random Variables.- Conditional Probability Mass Functions.- Discrete N-dimensional Random Variables.- Continuous Random Variables.- Expected Values for Continuous Random Variables.- Multiple Continuous Random Variables.- Conditional Probability Density Functions.- Continuous N-dimensional Random Variables.- Probability and Moment Approximations Using Limit Theorems.- Basic Random Processes.- Wide Sense Stationary Random Processes.- Linear Systems and Wide Sense Stationary Random Processes.- Multiple Wide Sense Stationary Random Processes.- Gaussian Random Processes.- Poisson Random Processes.- Markov Chains.- Appendix A: Glossary of Symbols and Abbreviations.- Appendix B: Assorted Math Facts and Formulas.- Appendix C: Linear and Matrix Algebra.- Appendix D: Summary of Signals, Linear Transforms, and Linear Systems.- Appendix E: Answers to Selected Problems