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With the advent of faster computers with comparatively largestorage facilities, simulation-based modeling is rapidly beingadopted as an alternative approach to conventional top-down, assumptions-based, continuous, stochastic and discrete differentialequation modeling. Thompson offers an interesting exposition to theart of simulation, and views „simulation approach“ to modeling as aparadigm for realistic evolutionary modeling. The book is writtenin a very casual style, and background knowledge in statistics isall that is required to grasp the material contained therein. Thompson begins with an exposition of the generation ofrandomnumbers and then delves into a variety of special topics, including models for stocks and derivatives, optimization andestimation in a noisy world, Monte-Carlo solutions to differentialequations, simulation assessment of multivariate and robustprocedures in statistical process control, resampling-based tests, and some exposition to modeling the AIDS epidemic. Several usefulalgorithms, problem sets, and references to standard simulationpackages are provided. Short chapter bibliographies; wide range ofexamples. The book could be used as a resource for beginninggraduate students and professionals in applied statistics, computerscience, economics and finance, engineering, and the naturalsciences. Highly recommended. Graduate students; faculty; professionals. (CHOICE, April 2001, Vol. 38, No. 8)
„... an eclectic survey of computing methods... lively andinteresting... the wide variety of example certainly helps bring thematerial to life.“ (Journal of the American StatisticalAssociation, Vol. 97, No. 457, March 2002)
„... a very useful and entertaining book... a great referencebook... contains some valuable material and philosophy that isunavailable anywhere else.“ (IIE Transactions)
„... often entertaining... the level of detain and relevance isappropriate... a worthwhile read for model builders comfortable withboth mathematics and simulation.“ (Complexity, Vol. 7, No. 2, 2002)
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.
