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Mathematical Modeling. Edition No. 4

  • ID: 2252771
  • Book
  • March 2013
  • 384 Pages
  • Elsevier Science and Technology
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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.

  • Offers increased support for instructors, including MATLAB material as well as other on-line resources
  • Features new sections on time series analysis and diffusion models
  • Provides additional problems with international focus such as whale and dolphin populations, plus updated optimization problems

Please Note: This is an On Demand product, delivery may take up to 11 working days after payment has been received.

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I. OPTIMIZATION MODELS
1. One-Variable Optimization
2. Multivariable Optimization
3. Computational Methods for Optimization

II. DYNAMIC MODELS
4. Introduction to Dynamic Models
5. Analysis of Dynamic Models
6. Simulation of Dynamic Models

III. PROBABILITY MODELS
7. Introduction to Probability Models
8. Stochastic Models
9. Simulation of Probability Models
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Meerschaert, Mark
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
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