Mathematical Modeling
- 5th Edition - September 1, 2029
- Latest edition
- Authors: Jennifer M. Switkes, Mark Meerschaert
- Language: English
- Paperback ISBN:9 7 8 - 0 - 3 2 3 - 9 8 4 0 8 - 9
- eBook ISBN:9 7 8 - 0 - 3 2 3 - 9 8 4 0 9 - 6
From genetic engineering to hurricane prediction, mathematical models increasingly guide much of the decision-making in our world. If the assumptions and methods underlying the mo… Read more
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From genetic engineering to hurricane prediction, mathematical models increasingly guide much of the decision-making in our world. If the assumptions and methods underlying the modeling are flawed, the outcome can be disastrously poor. With mathematical modeling growing rapidly in many scientific and technical disciplines, Mathematical Modeling provides reliable overviews of major modeling techniques to guide quality application in appropriate settings.
Now in its Fifth Edition, Mathematical Modeling offers a trusted survey for mathematical modeling courses, with online instructor support and updated problems. This valued text explores a range of approaches, including optimization models, dynamic models, and probability models. Each module begins with a real-world problem, followed by a discussion of techniques, solving the problem using the described technique, and interpretation of the results. Through this practical and clear coverage,
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Features sections on time series analysis and diffusion models
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Includes additional problems on real world applications and optimization problems
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Offers instructor support material, including MATLAB material
Primary market/audience (and market size, if known)
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Students (~4,000-4,500 students/year, in 4-year colleges tracked by NavStem) at the advanced undergraduate / early graduate level
Secondary market/audience:
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Researchers/Professionals
I. OPTIMIZATION MODELS 1. One-Variable Optimization 2. Multivariable Optimization 3. Computational Methods for Optimization II. DYNAMIC MODELS Chapter 4. Introduction to Dynamic Models Chapter 5. Analysis of Dynamic Models Chapter 6. Simulation of Dynamic Models III. PROBABILITY MODELS 7. Introduction to Probability Models 8. Stochastic Models 9. Simulation of Probability Models
- Edition: 5
- Latest edition
- Published: September 1, 2029
- Language: English
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Jennifer M. Switkes
Jennifer M. Switkes is Professor and Associate Chair of the Department of Mathematics and Statistics at California State Polytechnic University, Pomona. After earning her doctoral from Claremont Graduate College in Mathematical Biology, she taught at Citrus College and the University of Redlands before joining Cal Poly Pomona in 2001. Her teaching focuses on project-based education of students, centered around real-world applications of mathematical modeling. She was awarded the Deborah and Franklin Haimo Awards for Distinguished College or University Teaching of Mathematics, the highest teaching honor given by Mathematical Association of America, "for bringing her educational core values of excellence, honor, integrity, love, and purpose to all students, and specifically to traditionally underserved students". The award recognized both her mentorship of undergraduate and master's students at her university as well as her inspiring volunteer work teaching mathematics in prisons.
Affiliations and expertise
Professor and Associate Department Chair, Department of Mathematics & Statistics, California State Polytechnic University, Pomona, CA USAMM
Mark Meerschaert
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
Affiliations and expertise
University Distinguished Professor, Michigan State University, East Lansing, MI, USA