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Modeling Uncertainty An Examination of Stochastic Theory, Methods, and Applications (International Series in Operations Research and Management ... in Operations Research & Management Science)-[1]-[2002]-[pdf]-[Moshe Dror, Pierre L'Ecuyer, Ferenc Szidarovszky]

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发表于 2020-2-10 22:02:10 | 显示全部楼层 |阅读模式
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书籍信息:
书名: Modeling Uncertainty: An Examination of Stochastic Theory, Methods, and Applications (International Series in Operations Research and Management ... in Operations Research & Management Science)
语言: English
格式: pdf
大小: 33.8M
页数: 781
年份: 2002
作者: Moshe Dror, Pierre L'Ecuyer, Ferenc Szidarovszky
版次: 1
出版社: Springer

简介

Modeling Uncertainty: An Examination of Stochastic Theory, Methods, and Applications, is a volume undertaken by the friends and colleagues of Sid Yakowitz in his honor. Fifty internionally known scholars have collectively contributed 30 papers on modeling uncertainty to this volume. Each of these papers was carefully reviewed and in the majority of cases the original submission was revised before being accepted for publication in the book. The papers cover a great variety of topics in probability, statistics, economics, stochastic optimization, control theory, regression analysis, simulation, stochastic programming, Markov decision process, application in the HIV context, and others. There are papers with a theoretical emphasis and others that focus on applications. A number of papers survey the work in a particular area and in a few papers the authors present their personal view of a topic. It is a book with a considerable number of expository articles, which are accessible to a nonexpert - a graduate student in mathematics, statistics, engineering, and economics departments, or just anyone with some mathematical background who is interested in a preliminary exposition of a particular topic. Many of the papers present the state of the art of a specific area or represent original contributions which advance the present state of knowledge. In sum, it is a book of considerable interest to a broad range of academic researchers and students of stochastic systems.

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