- Mark Meerschaert, Michigan State University, East Lansing, MI, USA
Meerschaert's new edition strengthens his position as the survey text of choice for mathematical modeling courses, adding ample instructor support and leveraging on-line 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.
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
"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