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Zusatztext "...resource for beginning graduate students and professionals in applied statistics! computer science! economics and finance! engineering and the natural sciences. Highly recommended." (Choice! Vol. 38! No. 8! April 2001)"...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) Informationen zum Autor 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. Klappentext 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. Zusammenfassung This text uses simulation as a computational aid in dealing with and creating models of reality. Its main objective is to incorporate simulation as an integral part of the interaction between data and the models which may have approximately generated them. Inhaltsverzeichnis 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 Co...
