- Mark Meerschaert
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.
Advanced undergraduate or beginning graduate students in mathematics and closely related fields. Formal prerequisites consist of the usual freshman-sophomore sequence in mathematics, including one-variable calculus, multivariable calculus, linear algebra, and differential equations. Prior exposure to computing and probability and statistics is useful, but is not required.
Hardbound, 384 Pages
Published: January 2013
Imprint: Academic Press
"Meerschaert presents a general introduction to mathematical modeling for advanced undergraduate or beginning graduate students in mathematics and closely related fields He does challenge students to use all the mathematics they have learned as he covers modeling problems in optimization, dynamical systems, and stochastic processes."-- Reference & Research Book News,October 2013 "I think this is the best book in its genre. I haven't been tempted to use another. The mathematics in it is interesting, useful, and still within reach of typical undergraduates." -- John E. Doner,Department of Mathematics, University of California, Santa Barbara
- 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