Customer Reviews

Mathematical Modeling, Third Edition

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4 star:		(1)
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In this book you'll find, fully explained, subjects that are only mentioned briefly in other texts on modeling. I liked the chapter (chapter five) on Eigenvalue Methods for the analysis of dynamic models. I wished I had it four years ago when I was taking a course on modeling at the University of Washington. It really shows you how to do it. The same can be said about chapters 2 and 3 on optimization and chapters 7 and 8 on probability models (I liked specially the one on Markov Chains). I'm using it for teaching those techniques. The pseuodocode it includes is particularly helpful when you take the methods presented in the book to the computer. However, I would not recomend it to be used on its own as the only text in a course. It is a nice complement to texts like Gurney and Nisbet's Ecological Dynamics that presents many particular models in detail, while Meerschaert makes enphasis on general techniques. It's also a step up the ladder from "abc-of-modeling-techniques" texts like Vandermeer's Elementary Mathemathical Ecology. With 20/20 hindsight, if I had to learn modeling from the begining (or, if I had to teach it as I'm doing now) I would choose these titles as a step-by-step system, coupled with some Math packages like MathCad or Matlab.

Like the title says, this book is great for reviewing mathematical concepts. Great examples of how to do lagrange multipliers and other basics of mathematical concepts and makes it interesting, this book helps you apply what you have learned in your math classes to mathematical models such as growth incentives and revenue.

This a good book for beginners who want to start Math Models. This book, starts with actual problem with numbers and so is not abstract. It helps my students understand the concepts better

The book is well organized, smooth in its presentation, clear in its language and presents the topics in a good growing natural maturing sequence. I was initially intimidated by the subject and worried that the math might be dry and/or hard to digest. To the contrary, the book proceeds in a natural manner from simple to mature (not hard) covering from linear programming to dynamic models to stochastic probabilistic models in a very clear compelling and convincing manner all the way explaining why and how the need for the new model emerged. Surprisingly it is not a a long read too as it is a mere 350 pages, but the book is a rich clear reading experience I would say. I couldn't be more satisfied than this, with a book on this subject.

The other notable thing about the book which shines out immediately considering the concise number of pages (~350 pages) is that the author focuses on the meat of the subject right from the very beginning, straight to the point with real life examples, no wasting of your time, just the real subject matter. No proofs or side tracking; just as the books title says: mathematical modeling.

For me coming form artificial intelligence background with data mining, I needed to learn the topic as soon as possible for work related needs. The book served me the most. Truly brilliant. Thanks.

If you want to learn how you can apply what you learned in your differential equations or introductory statistics classes, this is the perfect book to start with. The book is divided clearly into three separate techniques for modelling: optimization models, dynamical models, and probabilistic models. Each technique is broadly covered and provides references to authoritative texts in the field for those who seek a more rigorous treatment. Each chapter includes a running example from various fields (engineering, ecology, circuits, etc...) along with some coverage of numerical methods that is helpful in learning how to actually apply/solve what is learned. It also includes some code, which can be very useful for beginners like me who have not used programs like Mathematica. I used this for a modelling course and had an instructor, but it is definitely possible to use this book for self-studying, as well.