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An Introduction to Mathematical Modeling (Dover Books on Computer Science) Paperback – March 6, 2000

ISBN-13: 978-0486411804 ISBN-10: 048641180X

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An Introduction to Mathematical Modeling (Dover Books on Computer Science) + The Art of Modeling Dynamic Systems: Forecasting for Chaos, Randomness and Determinism (Dover Books on Computer Science) + Stochastic Modeling: Analysis and Simulation (Dover Books on Mathematics)
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Product Details

  • Series: Dover Books on Computer Science
  • Paperback: 272 pages
  • Publisher: Dover Publications (March 6, 2000)
  • Language: English
  • ISBN-10: 048641180X
  • ISBN-13: 978-0486411804
  • Product Dimensions: 0.5 x 5.5 x 9 inches
  • Shipping Weight: 9.6 ounces (View shipping rates and policies)
  • Average Customer Review: 4.3 out of 5 stars  See all reviews (7 customer reviews)
  • Amazon Best Sellers Rank: #227,502 in Books (See Top 100 in Books)

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Most Helpful Customer Reviews

41 of 42 people found the following review helpful By Peter Soucy on September 3, 2002
Format: Paperback
Aimed at senior level undergraduates, the first chapter briefly discusses at a high level what mathematical models are, how they¡¯re formulated and rules of thumb as to how to evaluate them. The rest of the book surveys simple to moderately complex models applied to problems taken from a wide variety of disciplines in business, science, and engineering. As a survey course, brevity and breadth take precedent over depth and the examples are watered-down versions of problems taken from a plethora of sources cited throughout the text. However, the problems and models are not too superficial that they don¡¯t retain the essential issues modelers are likely to encounter.
Although the book is intended primarily for college seniors and first year graduate students, ¡°Part I: Elementary Methods¡± requires only first year calculus and basic probability whereas ¡°Part II: More Advanced Methods¡± also requires differential equations. Therefore, the book will appeal to various levels.
The book is rather dated as is evident by its lack of emphasis on numerical methods and no one should expect to be ready for any serious real world modeling as a result of reading this text alone. However, the book does not pretend to be anything more than what it is and the author cautions that it should merely supplement and not substitute mathematics and science coursework. (I would also add that a few courses in numerical methods and computer science would also be the order of the day.)
Although the first chapter outlines a quick four-step process for formulating mathematical models, the author stresses the role of discussion and research behind each high level step. Any attempt to provide detailed cookbook heuristics would be a sham.
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33 of 37 people found the following review helpful By L. Wathieu on August 25, 2000
Format: Paperback
I read this book for the first time when I was in college. It addresses many fundamental and practical questions with tremendous clarity. It particularly stands out in my mind because of its simple and compelling answers to three questions: Why do we need models of reality? What are the constraints on rigorous modelling (trade-offs between precision vs. generality vs.simplicity)? How to evaluate a mathematical model?
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4 of 4 people found the following review helpful By Secluded Path on July 1, 2010
Format: Paperback
If you are curious about mathematical modelling and do not know where to start this book is surely for you. This little, old and inexpensive volume will effectively introduce you to the basics of mathematical modelling. It is true that is dated now, as it was first published in 1978, but I will always prefer and recommend it over other extremely expensive books out in the market.
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2 of 2 people found the following review helpful By calvinnme HALL OF FAMETOP 1000 REVIEWERVINE VOICE on March 22, 2008
Format: Paperback
There are so many expensive books on this subject, this gives you a concise introduction to the field of mathematical modeling that is informative and interesting. The author doesn't tackle the mathematical models of any one field. Instead he tries to illustrate the process of designing and analyzing mathematical models by showing examples from a variety of disciplines. There are plenty of examples in this book, but don't expect recipes, because there really is no such thing as a mathematical modeling recipe. There are multiple approaches and thus multiple answers to just about every modeling problem. What the author does is make you comfortable with these facts and give you some questions to ask when approaching any modeling problem. Anybody with an understanding of calculus and ordinary differential equations and maybe a dash of probability theory should feel at home with this book. Highly recommended, especially before tackling some of the more expensive and specialized books on this subject.
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