James Thompson – Simulation. A Modeler’s Approach

Description

James Thompson – Simulation. A Modeler’s Approach

Description

A unique, integrated treatment of computer modeling and simulation “The future of science belongs to those willing to make the shift to simulation-based modeling,” predicts Rice Professor James Thompson, a leading modeler and computational statistician widely known for his original ideas and engaging style. He discusses methods, available to anyone with a fast desktop computer, for integrating simulation into the modeling process in order to create meaningful models of real phenomena. Drawing from a wealth of experience, he gives examples from trading markets, oncology, epidemiology, statistical process control, physics, public policy, combat, real-world optimization, Bayesian analyses, and population dynamics.

Table of Contents

The Generation of “Random” Numbers.

Random Quadrature.

Monte Carlo Solutions of Differential Equations.

Markov Chains, Poisson Processes and Linear Equations.

SIMEST, SIMDAT, and Pseudoreality.

Models for Stocks and Derivatives.

Simulation Assessment of Multivariate and Robust Procedures in Statistical Process Control.

Noise and Chaos.

Bayesian Approaches.

Resampling Based Tests.

Optimization and Estimation in a Noisy World.

Modeling the USA AIDS Epidemic: Exploration, Simulation and Conjecture.

Appendices.

Index.

Author Information

JAMES R. THOMPSON, PhD, is Professor of Statistics at Rice University. A Fellow of the American Statistical Association and the Institute of Mathematical Statistics, he is an elected member of the International Statistical Institute. In 1985, he received the ASA’s Don Owen Award, and in 1991, he was awarded the U.S. Army’s Samuel S. Wilks Medal for his work in applied statistics. A frequent consultant to industry, he holds adjunct professorships at the M. D. Anderson Cancer Center and the University of Texas School of Public Health. He is the author of ten books, including Empirical Model Building, available from Wiley.

Reviews

With the advent of faster computers with comparatively large storage facilities, simulation-based modeling is rapidly being adopted as an alternative approach to conventional top-down, assumptions-based, continuous, stochastic and discrete differential equation modeling. Thompson offers an interesting exposition to the art of simulation, and views “simulation approach” to modeling as a paradigm for realistic evolutionary modeling. The book is written in a very casual style, and background knowledge in statistics is all that is required to grasp the material contained therein.

“…an eclectic survey of computing methods…lively and interesting…the wide variety of example certainly helps bring the material to life.” (Journal of the American Statistical Association, Vol. 97, No. 457, March 2002)

“…a very useful and entertaining book…a great reference book…contains some valuable material and philosophy that is unavailable anywhere else.” (IIE Transactions)

“…often entertaining…the level of detain and relevance is appropriate…a worthwhile read for model builders comfortable with both mathematics and simulation.” (Complexity, Vol. 7, No. 2, 2002)

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