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A course in mathematical modeling
Synopses & Reviews
The emphasis of this book lies on the teaching of mathematical modeling rather than simply presenting models. To this end the book starts with the simple discrete exponential growth model as a building block, and successively refines it. This involves adding variable growth rates, multiple variables, fitting growth rates to data, including random elements, testing exactness of fit, using computer simulations and moving to a continuous setting. No advanced knowledge is assumed of the reader, making this book suitable for elementary modeling courses. The book can also be used to supplement courses in linear algebra, differential equations, probability theory and statistics.
Book News Annotation:
This text is intended for students who have mastered a one-year course in calculus. Emphasizing the teaching of the modeling process as opposed to merely presenting models, it balances a variety of opposing modeling methodologies including theoretical versus empirical, analytical versus simulation, deterministic versus stochastic, and discrete versus continuous. Six chapters present the simple discrete exponential growth model which is then refined to include variable growth rates, multiple variables, growth rates fitted to data, the effects of random factors, and continuous-time models. Annotation c. by Book News, Inc., Portland, OR (email@example.com)
Teaches elementary mathematical modelling.
The aim of this book is the teaching of mathematical modeling. No advanced knowledge is assumed of the reader, making this book suitable for elementary modeling courses, or to supplement courses in linear algebra, differential equations, probability theory and statistics.
This book teaches elementary mathematical modeling.
Includes bibliographical (p. 423-426) references and index.
Table of Contents
Modeling basics : purpose, resolution, and resources — Discrete dynamical systems — Discrete stochasticity — Stages, states, and classes — Empirical modeling — Continuous models — Continuous stochasticity.
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