Description
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.
A well-balanced survey of modeling methods with challenging cross-disciplinary problems and new examples
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
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
Book Information
ISBN 9780123869128
Author Mark Meerschaert
Format Hardback
Page Count 384
Imprint Academic Press Inc
Publisher Elsevier Science Publishing Co Inc
Weight(grams) 730g
A well-balanced survey of modeling methods with challenging cross-disciplinary problems and new examples
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
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
Book Information
ISBN 9780123869128
Author Mark Meerschaert
Format Hardback
Page Count 384
Imprint Academic Press Inc
Publisher Elsevier Science Publishing Co Inc
Weight(grams) 730g
