This textbook is intended for one-semester courses in stochastic processes for students familiar with elementaiy probability theory and calculus. The objectives of the book are to introduce students to the standard conr,epts 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. This revised edition includes twice the number of exercises as the f irst edition, many of which are applications problems, and several sections have been rewritten for clarity An Introduction to Stochastic Modeling, Revised Edition provides information pertinent to the standard concepts and methods of stochastic modeling. This book presents the rich diversity of applications of stochastic processes in the sciences. Organized into nine chapters, this book begins with an overview of diverse types of stochastic models, which predicts a set of possible outcomes weighed by their likelihoods or probabilities. This text then provides exercises in the applications of simple stochastic analysis to appropriate problems. Other chapters consider the study of general functions of independent, identically distributed, nonnegative random variables representing the successive intervals between renewals. This book discusses as well the numerous examples of Markov branching processes that arise naturally in various scientific disciplines. The final chapter deals with queueing models, which aid the design process by predicting system performance. This book is a valuable resource for students of engineering and management science. Engineers will also find this book useful. Content: Front Matter, Page iii Copyright, Page iv Preface, Page ix To the Instructor, Page x Revised Edition, Page x Acknowledgments, Page xi Chapter 1 - Introduction, Pages 1-58 Chapter 2 - Conditional Probability and Conditional Expectation, Pages 59-88 Chapter 3 - Markov Chains: Introduction, Pages 89-165 Chapter 4 - The Long Run Behavior of Markov Chains, Pages 167-240 Chapter 5 - Poisson Processes, Pages 241-300 Chapter 6 - Continuous Time Markov Chains, Pages 301-387 Chapter 7 - Renewal Phenomena, Pages 389-435 Chapter 8 - Branching Processes and Population Growth, Pages 437-483 Chapter 9 - Queueing Systems, Pages 485-541 Further Readings, Pages 543-544 Solutions to Selected Exercises, Pages 545-562 Index, Pages 563-566 "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."--Publisher description (LoC). This textbook is intended for one-semester courses in stochastic processes for students familiar with elementary probability theory and calculus. This revised edition includes twice the number of exercises as the first edition, many of which are application problems.
