國立虎尾科技大學 |

The Design and Analysis of Computer Experiments

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	The Design and Analysis of Computer Experiments/ by Thomas J. Santner, Brian J. Williams, William I. Notz.
作者:	Santner, Thomas J.
其他作者:	Williams, Brian J.
面頁冊數:	XV, 436 p. 123 illus., 62 illus. in color.online resource. :
Contained By:	Springer Nature eBook
標題:	Statistics . -
電子資源:	https://doi.org/10.1007/978-1-4939-8847-1
ISBN:	9781493988471

The Design and Analysis of Computer Experiments
Santner, Thomas J.

The Design and Analysis of Computer Experiments[electronic resource] /by Thomas J. Santner, Brian J. Williams, William I. Notz. - 2nd ed. 2018. - XV, 436 p. 123 illus., 62 illus. in color.online resource. - Springer Series in Statistics,0172-7397. - Springer Series in Statistics,.

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.

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.

ISBN: 9781493988471

Standard No.: 10.1007/978-1-4939-8847-1doiSubjects--Topical Terms:

1253516
Statistics .

LC Class. No.: QA276-280

Dewey Class. No.: 519.5

The Design and Analysis of Computer Experiments
LDR:03793nam a22003975i 4500 001 988920
003 DE-He213
005 20200706235748.0
007 cr nn 008mamaa
008 201225s2018 xxu| s |||| 0|eng d
020 $a 9781493988471 $9 978-1-4939-8847-1
024 7 $a 10.1007/978-1-4939-8847-1 $2 doi
035 $a 978-1-4939-8847-1
050 4 $a QA276-280
072 7 $a PBT $2 bicssc
072 7 $a MAT029000 $2 bisacsh
072 7 $a PBT $2 thema
082 0 4 $a 519.5 $2 23
100 1 $a Santner, Thomas J. $4 aut $4 http://id.loc.gov/vocabulary/relators/aut $3 1211801
245 1 4 $a The Design and Analysis of Computer Experiments $h [electronic resource] / $c by Thomas J. Santner, Brian J. Williams, William I. Notz.
250 $a 2nd ed. 2018.
264 1 $a New York, NY : $b Springer New York : $b Imprint: Springer, $c 2018.
300 $a XV, 436 p. 123 illus., 62 illus. in color. $b online resource.
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
347 $a text file $b PDF $2 rda
490 1 $a Springer Series in Statistics, $x 0172-7397
505 0 $a 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.
520 $a 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.
650 0 $a Statistics . $3 1253516
650 0 $a Applied mathematics. $3 1069907
650 0 $a Engineering mathematics. $3 562757
650 1 4 $a Statistical Theory and Methods. $3 671396
650 2 4 $a Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences. $3 782247
650 2 4 $a Mathematical and Computational Engineering. $3 1139415
700 1 $a Williams, Brian J. $4 aut $4 http://id.loc.gov/vocabulary/relators/aut $3 1211802
700 1 $a Notz, William I. $4 aut $4 http://id.loc.gov/vocabulary/relators/aut $3 685746
710 2 $a SpringerLink (Online service) $3 593884
773 0 $t Springer Nature eBook
776 0 8 $i Printed edition: $z 9781493988457
776 0 8 $i Printed edition: $z 9781493988464
830 0 $a Springer Series in Statistics, $x 0172-7397 $3 1257229
856 4 0 $u https://doi.org/10.1007/978-1-4939-8847-1
912 $a ZDB-2-SMA
912 $a ZDB-2-SXMS
950 $a Mathematics and Statistics (SpringerNature-11649)
950 $a Mathematics and Statistics (R0) (SpringerNature-43713)