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The Design and Analysis of Computer Experiments

  • Thomas J.  Santner
  • Brian J. Williams
  • William I.  Notz

Part of the Springer Series in Statistics book series (SSS)

Table of contents

  1. Front Matter
    Pages i-xv
  2. Thomas J. Santner, Brian J. Williams, William I. Notz
    Pages 1-26
  3. Thomas J. Santner, Brian J. Williams, William I. Notz
    Pages 27-66
  4. Thomas J. Santner, Brian J. Williams, William I. Notz
    Pages 67-114
  5. Thomas J. Santner, Brian J. Williams, William I. Notz
    Pages 115-143
  6. Thomas J. Santner, Brian J. Williams, William I. Notz
    Pages 145-200
  7. Thomas J. Santner, Brian J. Williams, William I. Notz
    Pages 201-246
  8. Thomas J. Santner, Brian J. Williams, William I. Notz
    Pages 247-297
  9. Thomas J. Santner, Brian J. Williams, William I. Notz
    Pages 299-379
  10. Back Matter
    Pages 381-436

About this book

Introduction

This book describes methods for designing and analyzing experiments that are conducted using a computer code, a computer experiment, and, when possible, a physical experiment. Computer experiments continue to increase in popularity as surrogates for and adjuncts to physical experiments. Since the publication of the first edition, there have been many methodological advances and software developments to implement these new methodologies. The computer experiments literature has emphasized the construction of algorithms for various data analysis tasks (design construction, prediction, sensitivity analysis, calibration among others), and the development of web-based repositories of designs for immediate application. While it is written at a level that is accessible to readers with Masters-level training in Statistics, the book is written in sufficient detail to be useful for practitioners and researchers.

 

New to this revised and expanded edition:

• An expanded presentation of basic material on computer experiments and Gaussian processes with additional simulations and examples     

• A new comparison of plug-in prediction methodologies for real-valued simulator output

• An enlarged discussion of space-filling designs including Latin Hypercube designs (LHDs), near-orthogonal designs, and nonrectangular regions

• A chapter length description of process-based designs for optimization, to improve good overall fit, quantile estimation, and Pareto optimization

• A new chapter describing graphical and numerical sensitivity analysis tools

• Substantial new material on calibration-based prediction and inference for calibration parameters

•  Lists of software that can be used to fit models discussed in the book to aid practitioners


Keywords

Gaussian Process models stochastic process models computer experiment best linear unbiased predictors simulator output Bayesian inference experimental design Latin hypercube designs heuristic global approximation sensitivity analysis variable screening calibration log likelihood functions

Authors and affiliations

  • Thomas J.  Santner
    • 1
  • Brian J. Williams
    • 2
  • William I.  Notz
    • 3
  1. 1.Department of StatisticsThe Ohio State UniversityColumbusUSA
  2. 2.Statistical Sciences GroupLos Alamos National LaboratoryLos AlamosUSA
  3. 3.Department of StatisticsThe Ohio State UniversityColumbusUSA

Bibliographic information

  • DOI https://doi.org/10.1007/978-1-4939-8847-1
  • Copyright Information Springer Science+Business Media, LLC, part of Springer Nature 2018
  • Publisher Name Springer, New York, NY
  • eBook Packages Mathematics and Statistics
  • Print ISBN 978-1-4939-8845-7
  • Online ISBN 978-1-4939-8847-1
  • Series Print ISSN 0172-7397
  • Series Online ISSN 2197-568X
  • Buy this book on publisher's site
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