Mathematical Modeling

by Mark Meerschaert

Overview

The new edition of Mathematical Modeling, the survey text of choice for mathematical modeling courses, adds ample instructor support and online delivery for solutions manuals and software ancillaries.

From genetic engineering to hurricane prediction, mathematical models guide much of the decision making in our society. If the assumptions and methods underlying the modeling are flawed, the outcome can be disastrously poor. With mathematical modeling growing rapidly in so many scientific and technical disciplines, Mathematical Modeling, Fourth Edition provides a rigorous treatment of the subject. The book explores a range of approaches including optimization models, dynamic models and probability models.

Product Details

ISBN-13:	9780123869128
Publisher:	Elsevier Science & Technology Books
Publication date:	01/28/2013
Edition description:	4th Revised ed.
Pages:	384
Product dimensions:	6.20(w) x 9.10(h) x 1.00(d)

About the Author

Mark M. Meerschaert is Chairperson of the Department of Statistics and Probability at Michigan State University and Adjunct Professor in the Department of Physics at the University of Nevada, having previously worked in government and industry roles on a wide variety of modeling projects. Holding a doctorate in Mathematics from the University of Michigan, Professor Meerschaert’s expertise spans the areas of probability, statistics, statistical physics, mathematical modeling, operations research, partial differential equations, and hydrology. In addition to his current appointments, he has taught at the University of Michigan, Albion College, and the University of Otago, New Zealand. His current research interests include limit theorems and parameter estimation for infinite variance probability models, heavy tail models in finance, modeling river flows with heavy tails and periodic covariance structure, anomalous diffusion, continuous time random walks, fractional derivatives and fractional partial differential equations, and ground water flow and transport. For more, see http://www.stt.msu.edu/~mcubed

Table of Contents

I. OPTIMIZATION MODELS 1. One-Variable Optimization2. Multivariable Optimization3. Computational Methods for OptimizationII. DYNAMIC MODELS 4. Introduction to Dynamic Models5. Analysis of Dynamic Models6. Simulation of Dynamic ModelsIII. PROBABILITY MODELS 7. Introduction to Probability Models8. Stochastic Models9. Simulation of Probability Models

What People are Saying About This

From the Publisher

A well-balanced survey of modeling methods with challenging cross-disciplinary problems and new examples

Editorial Reviews

"This book distinguishes itself from comparable texts by its broad treatment of the field. It offers an extensive survey of mathematical modeling problems and techniques that is organized into three big sections corresponding to optimization, dynamics and probability models." —MAA Reviews, March 19, 2014

From the Publisher

The author introduces a five-step approach to mathematical modeling, which he demonstrates throughout the text. A variety of techniques and programs, such as Mathematica, MAPLE, and MINITAB, are used to solve a spectrum of realistic applications, from optimization to dynamical systems to stochastic processes. The book is divided into sections on each major type of mathematical model: optimization models, dynamic models, and probability models. Each chapter includes exercises. Annotation c. Book News, Inc., Portland, OR (booknews.com)

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