Synopses & Reviews
This book presents applied probability and stochastic processes in an elementary but mathematically precise manner, with numerous examples and exercises to illustrate the range of engineering and science applications of the concepts. The book is designed to give the reader an intuitive understanding of probabilistic reasoning, in addition to an understanding of mathematical concepts and principles. The initial chapters present a summary of probability and statistics and then Poisson processes, Markov chains, Markov processes and queuing processes are introduced. Advanced topics include simulation, inventory theory, replacement theory, Markov decision theory, and the use of matrix geometric procedures in the analysis of queues. Included in the second edition are appendices at the end of several chapters giving suggestions for the use of Excel in solving the problems of the chapter. Also new in this edition are an introductory chapter on statistics and a chapter on Poisson processes that includes some techniques used in risk assessment. The old chapter on queues has been expanded and broken into two new chapters: one for simple queuing processes and one for queuing networks. Support is provided through the web site http://apsp.tamu.edu where students will have the answers to odd numbered problems and instructors will have access to full solutions and Excel files for homework.
Review
amazon.con Customer Reviews on 1st edition: 5.0 out of 5 stars It's worth at least six stars!, February 19, 2000 By Lisa Ann Ball (Seattle, WA United States) - See all my reviews I randomly ran across this book in my math library trying to find an extra book to help with the difficult Stochastics Process class I was taking. Little did I know I would find a book I value as much as Douglas Kelly's Introduction to Probability. This book has applied problems and examples! It is not the dry, endless pages of confusing equations we have come to expect from Stochastics Processes books. There is something better out there! This book saved me as an undergraduate, and am now looking forward to it living up to my God like expectations as a post grad. If you are a professor, please use this book for you students. It ties together and lets you appreciate many fields such as linear analysis and even graph theory from computer science. This book will not disappoint. 5.0 out of 5 stars Best introductory book, February 22, 2001 By Brad (Austin, TX USA) - See all my reviews Extremely clear, and easy to understand. It is the best introductory book on stochastic processes for non-mathematics major. After you read this book (one month is enough, how amazing it is!), it becomes easier to read "the first course in stochastic processes". The book focuses on the concept and intuition, instead of proof, and I find it is extremely useful for me -- CS major. Strong recommend this great book 5.0 out of 5 stars Good book! , August 13, 2007 By Yuan J. Son (sunnyvale, CA) - See all my reviews A lot of examples, easy to read. A lot of stochastic and queuing books are usually full of notations and theorems, thus hard to understand. However, the author of this book presented the materials in a way that we can actually understand the stochastic processes. If you want to learn queuing and do not have much background, this is the book!!!!
Synopsis
This book is a result of teaching stochastic processes to junior and senior undergr- uates and beginning graduate students over many years. In teaching such a course, we have realized a need to furnish students with material that gives a mathematical presentation while at the same time providing proper foundations to allow students to build an intuitive feel for probabilistic reasoning. We have tried to maintain a b- ance in presenting advanced but understandable material that sparks an interest and challenges students, without the discouragement that often comes as a consequence of not understanding the material. Our intent in this text is to develop stochastic p- cesses in an elementary but mathematically precise style and to provide suf?cient examples and homework exercises that will permit students to understand the range of application areas for stochastic processes. We also practice active learning in the classroom. In other words, we believe that the traditional practice of lecturing continuously for 50 to 75 minutes is not a very effective method for teaching. Students should somehow engage in the subject m- ter during the teaching session. One effective method for active learning is, after at most 20 minutes of lecture, to assign a small example problem for the students to work and one important tool that the instructor can utilize is the computer. So- times we are fortunate to lecture students in a classroom containing computers with a spreadsheet program, usually Microsoft's Excel.
About the Author
Richard M. Feldman is a Professor of Industrial and Systems Engineering at Texas A&M University. He received a B.A. degree in mathematics from Hope College, an M.S. degree in mathematics from Michigan State University, an M.S. degree in Industrial and Systems Engineering from Ohio University, and a Ph.D. in Industrial Engineering from Northwestern University. His teaching interests include simulation, applied probability, and queueing theory. His consulting and funded research activities have involved modeling and simulation within manufacturing, transportation, and biological contexts. He has received several teaching awards, published papers in applied probability and queueing theory, and co-authored four books. Ciriaco Valdez-Flores is senior risk assessment consultant at Sielken & Associates Consulting, Inc. He received a bachelor's degree from the Tecnológico at Cd. Victoria in México and master's and Ph.D. degrees from Texas A&M University, all in Industrial Engineering. He has taught graduate courses at Texas A&M University focusing in the areas of operations research and applied stochastic processes. As a consultant, he applies his background to the development of new methods of quantitative health risk assessment that incorporate simulation and decision tree theory. He has published in health risk assessment and stochastic processes, co-authored one book and has contributed to books in engineering economy and risk assessment.
Table of Contents
Basic Probability Review.- Basic of Monte Carlo Simulation.- Basic Statistical Review.- Poisson Processes.- Markov Chains.- Markov Processes.- Queueing Processes.- Queueing Networks.- Event-Driven Simulation and Output Analyses.- Inventory Theory.- Replacement Theory.- Markov Decision Processes.- Advanced Queues.- Matrix Review.- Index.