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Modeling Uncertainty

An Examination of Stochastic Theory, Methods, and Applications

  • Moshe Dror
  • Pierre L’Ecuyer
  • Ferenc Szidarovszky

Part of the International Series in Operations Research & Management Science book series (ISOR, volume 46)

Table of contents

  1. Front Matter
    Pages i-xxix
  2. Professor Sidney J. Yakowitz

    1. D. S. Yakowitz
      Pages 1-11
  3. Part I

  4. Part II

  5. Part III

    1. Arkadi Nemirovski, Reuven Y. Rubinstein
      Pages 156-184
    2. Benjamin Kedem, Konstantinos Fokianos
      Pages 185-199
    3. László Gyöfi, Gábor Lugosi
      Pages 225-248
  6. Part IV

    1. A. Haurie, F. Moresino
      Pages 269-283
    2. James A. Reneke, Matthew J. Saltzman, Margaret M. Wiecek
      Pages 301-331
    3. László Gerencsér
      Pages 359-371
  7. Part V

    1. Luc Devroye, Adam Krzyzak
      Pages 383-417
    2. Pierre L’Ecuyer, Christiane Lemieux
      Pages 419-474
  8. Part VI

  9. Part VII

    1. Philip J. Boland, Taizhong Hu, Moshe Shaked, J. George Shanthikumar
      Pages 607-623
    2. Paul J. Sanchez, John S. Ramberg, Larry Head
      Pages 651-684
  10. Part VIII

  11. Back Matter
    Pages 761-770

About this book

Introduction

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.

Keywords

Markov Chain Markov Chains Markov decision process Regression analysis SAS Statistical Methods Stochastic Approximation Stochastic Programming Stochastic Theory Stochastic model Stochastic modelling Time series mixing optimization

Editors and affiliations

  • Moshe Dror
    • 1
  • Pierre L’Ecuyer
    • 2
  • Ferenc Szidarovszky
    • 1
  1. 1.University of ArizonaUSA
  2. 2.Université de MontréalUSA

Bibliographic information

  • DOI https://doi.org/10.1007/b106473
  • Copyright Information Springer Science + Business Media, Inc. 2005
  • Publisher Name Springer, Boston, MA
  • eBook Packages Mathematics and Statistics
  • Print ISBN 978-0-7923-7463-3
  • Online ISBN 978-0-306-48102-4
  • Series Print ISSN 0884-8289
  • Buy this book on publisher's site