An Introduction to Stochastic Modeling 4th Edition by Mark A. Pinsky (PDF)

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User’s Reviews

Editorial Reviews: Amazon.com Review Serving as the foundation for a one-semester course in stochastic processes for students familiar with elementary probability theory and calculus, the fourth edition of Introduction to Stochastic Modeling 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.About This Edition In the fourth edition, we have added two new chapters: Chapter 10 on random evolution and Chapter 11 on characteristic functions. Chapter 10, “Random Evolution,” denotes a set of stochastic models, which describe continuous motion with piecewise linear sample functions. Explicit formulas are available in the simplest cases. In the general case, one has a central limit theorem, which is pursued more generally in Chapter 11, “Characteristic Functions and Their Applications.” Here the necessary tools from Fourier analysis are developed and applied when necessary. Many theorems are proved in full detail, while other proofs are sketched–in the spirit of the earlier Chapters 1-9. Complete proofs may be found by consulting the intermediate textbooks listed in the section on further reading. Instructors who have taught from the third edition may be reassured that Chapters 1-9 of the new edition are identical to the corresponding chapters of the new book.Changes This Edition Realistic applications from a variety of disciplines integrated throughout the text, including more biological applicationsPlentiful, completely updated problemsCompletely updated and reorganized end-of-chapter exercise sets, 250 exercises with answersNew chapters of stochastic differential equations and Brownian motion and related processesAdditional sections on Martingale and Poisson processRead a sample chapter from Introduction to Stochastic Modeling. Review PRAISE FOR THE SECOND EDITION “This book is a valuable resource for anyone studying combustion processes.” –David L. Liscinsky, United Technologist Research Center, in AIAA JOURNAL”This is an excellent text-book … The narrative is clear, careful and detailed but, at the same time, designed to draw (not to bore) the reader in. The main strengths, in my opinion, are the wealth of convincing applications, which are discussed at some, but not too much length after each bit of theoretical development, and the large number of exercises given at the ends of sections, not just at the ends of chapters.” –Martin Crowder, University of Surrey, Guildford, in THE STATISTICIAN Review This classic bestselling text serves as the foundation for a one-semester course in stochastic processing for students familiar with elementary probability theory and calculus Read more

Reviews from Amazon users which were colected at the time this book was published on the website:

⭐The language used in the book is not easy. The layout of the pages does not help to highlight important ideas. If you expect to learn from this book, expect to read each sentence, word for word.Granted, I was less familiar with the presentation of the beginning material. The probability class I took was taught from an axiomatic standpoint. We didn’t cover very much material, and we never discussed distributions. I felt like I was at a serious disadvantage when I embarked upon this class. The notation is different enough from the two probability books I have, and the whole approach was different from my previous class. I really wanted to understand this material (and I really don’t want to fail this class), so I did the only thing I could. Read the book. Slowly.It took me about 3 hours to fully read, understand, and solve problems related to pages 1-16.We’re halfway through Chapter 3 now, and I’m glad I spent so much time on those first pages. It really helps to clearly understand *exactly* what the early notation means. My instructor told me to skim Chapter 2, but to focus my time on Chapter 3, and that was sound advice. I reviewed their explanations of Bernoulli, Binomial, Geometric, and Poisson, as they seem to come up the most.At first, I hated this book (and my probability class for failing to prepare me). The upside of having to work so hard to understand, is that you do understand at the end of it. Thanks to understanding all the previous notation, Markov Chains (Chapter 3) was waaaaaaaay easier to read and solve. Waaaaaaaaay easier.The book has grown on me, and I don’t absolutely hate it anymore. I hope there’s an easier book out there, but maybe there isn’t. If your teacher picks this one, don’t panic. Just accept the fact that it’s going to be a slow process.The answers for most of the Exercises are in the back. The Solutions manual contains answers to the odd Problems. As a solutions manual, it sucks. Truly. But it’s not very expensive, and it might help you if you’re ever stuck on an odd problem.

⭐This book does not give enough description in the chapters. It is my belief that the author made the assumption that the consumers of this book know a lot more about the subject and doesn’t need much explaination. You should have a good background in probability before picking this up. Also the e-book solution manuel does not help, so don’t bother buying it.

⭐The book has four editions, I can hardly believe there are still so many mistakes in the book. As the book was published in 2010, and some of the authors passed away before that date, I assume the remained one is not able to organized the content well and check for the mistakes and typos in the book. Have anyone read the earlier edition of the book, are they better than this edition?

⭐This Fourth edition of Stochastic Modeling is a valuable addition to my library, and the solutions manual (kindle) is also helpful. Some form of recognition to Howard Taylor (co-author of Karlin in earlier editions) would have been appropriate. I hope that in future printings or editions the civility level is improved in the form of some acknowledgement of all earlier contributors.

⭐This book does not give much explanation and examples on each chapter. The problem is far more complex than what is discussed on the book.I end up getting other books to accompany with this book. Try to avoid this book if you can, I believe there are a lot more this kind of book with much better explanation out there.

⭐The book binding fell apart within the first couple of weeks. I’m using it a lot but it definitely should last a lot longer without falling apart.

⭐Compare to sheldon probability models , this is much easier book to read and understand. And when it comes to statistic everyone should be spending enough time to understand material. not a easy subject.

⭐This book may be useful with a dilligent instructor. However its not for do-it-yourselfers, or students who want a more in-depth look at the material. The majority of the time your wondering whats happening and you haven’t a clue whether you are doing exercises correctly. Not a great choice of text.

⭐Muy completo para cursos tanto de licenciatura como de posgrado

⭐Not found.

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