Mathematical Modeling

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

• 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

"This book distinguishes itself from comparable texts by its broad treatment of the field. It offers an extensive survey of mathematical modeling problems and techniques that is organized into three big sections corresponding to optimization, dynamics and probability models." --MAA Reviews, March 19, 2014