Mathematical Modeling

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.

Autorentext
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

Klappentext

Meerschaert's new edition strengthens his position as the survey text of choice for mathematical modeling courses, adding ample instructor support and leveraging 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, and if the assumptions and methods underlying the modeling are flawed, the outcome can be disastrously poor as recent events have proved. Since mathematical modeling is a rapidly growing specialty with applications in so many scientific and technical disciplines, there is a need for mathematically rigorous treatments of the subject and particularly for texts that expose students to a range of possible approaches.

Zusammenfassung
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. This book explores a range of approaches including optimization models, dynamic models and probability models.

Inhalt
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