The Design and Analysis of Computer Experiments by Thomas J. Santner, Brian J. Williams, William I. Notz

Format:

Book

Author/Creator:

Santner, Thomas J., Author.

Williams, Brian J., Author.

Notz, William I., Author.

Contributor:

SpringerLink (Online service)

Series:

Springer series in statistics 2197-568X

Springer Series in Statistics 2197-568X

Language:

English

Subjects (All):

Statistics.

Engineering mathematics.

Engineering--Data processing.

Engineering.

Statistical Theory and Methods.

Statistics in Engineering, Physics, Computer Science, Chemistry and Earth Sciences.

Mathematical and Computational Engineering Applications.

Local Subjects:

Statistical Theory and Methods.

Statistics in Engineering, Physics, Computer Science, Chemistry and Earth Sciences.

Mathematical and Computational Engineering Applications.

Physical Description:

XV, 436 pages 123 illustrations, 62 illustrations in color. online resource

Edition:

2nd edition 2018

Contained In:

Springer Nature eBook

Place of Publication:

New York, NY Springer New York Imprint: Springer 2018

System Details:

text file PDF

Summary:

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

Contents:

Physical Experiments and Computer Experiments

Stochastic Process Models for Describing Simulator Output

Empirical Best Linear Unbiased Prediction for Simulator Output

Bayesian Inference for Simulator Output

Space-Filling Designs for Computer Experiments

Some Criterion-based Experimental Designs

Sensitivity Analysis and Variable Screening

Calibration

Appendix A : List of Notation

Appendix B: Mathematical Facts

Appendix C: An Overview of Selected Optimization Algorithms

Appendix D: An Introduction to Markov Chain Monte Carlo Algorithms

Appendix E: A Primer on Constructing Quasi-Monte Carlo Sequences

Other Format:

Printed edition:

ISBN:

9781493988471

Access Restriction:

Restricted for use by site license