Simulation: A Modeler's Approach (Wiley Series in Probability and Statistics)

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. Dr. Thompson believes that, so far from liberating us from the necessity of modeling, the fast computer enables us to engage in realistic models of processes in , for example, economics, which have not been possible earlier because simple stochastic models in the forward temporal direction generally become quite unmanageably complex when one is looking for such things as likelihoods. Thompson shows how simulation may be used to bypass the necessity of obtaining likelihood functions or moment-generating functions as a precursor to parameter estimation. Simulation: A Modeler's Approach is a provocative and practical guide for professionals in applied statistics as well as engineers, scientists, computer scientists, financial analysts, and anyone with an interest in the synergy between data, models, and the digital computer.

Arguing that the future of science belongs to those willing to make the shift to simulation-based modeling, Thompson (statistics, Rice U.) examines 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. He cites examples from trading markets, oncology, epidemiology, statistical process control, physics, public policy, combat, real-world optimization, Bayesian analysis, and population dynamics. He assumes his readers already know how to model conventionally.
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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. Dr. Thompson believes that, so far from liberating us from the necessity of modeling, the fast computer enables us to engage in realistic models of processes in, for example, economics, which have not been possible earlier because simple stochastic models in the forward temporal direction generally become quite unmanageably complex when one is looking for such things as likelihoods. Thompson shows how simulation may be used to bypass the necessity of obtaining likelihood functions or moment-generating functions as a precursor to parameter estimation. Simulation: A Modeler’ s Approach is a provocative and practical guide for professionals in applied statistics as well as engineers, scientists, computer scientists, financial analysts, and anyone with an interest in the synergy between data, models, and the digital computer.

Dieses Buch will zeigen, wie sich Simulationen bei der Entwicklung geeigneter Modelle für reale Vorgänge nutzbringend anwenden lassen. Wichtigstes Ziel dabei ist es, die Simulation als Bindeglied zwischen Näherungswerten und Modellen einzusetzen, Hypothesen zu testen und ein Gefühl für die Variabilität der Daten zu bekommen. Zahlreiche Fallstudien fördern das konzeptuelle Denken. (01/00)

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.

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.