The Design and Analysis of Computer Experiments
Mentés helye:
| Szerző: | |
|---|---|
| Testületi szerző: | |
| Közreműködő(k): | |
| Különgyűjtemény: | e-book |
| Formátum: | könyv |
| Nyelv: | angol |
| Megjelenés: |
New York, NY : : Springer New York : : Imprint: Springer,,
2018
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| Kiadás: | 2nd ed. 2018. |
| Sorozat: | Springer Series in Statistics,, ISSN 0172-7397 |
| Tárgyszavak: | |
| Online elérés: | https://doi.org/10.1007/978-1-4939-8847-1 |
| Címkék: |
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| id |
opac-EUL01-000978894 |
|---|---|
| collection |
e-book |
| institution |
L_200 EUL01 |
| spelling |
Santner, Thomas J. . szerző aut http://id.loc.gov/vocabulary/relators/aut The Design and Analysis of Computer Experiments by Thomas J. Santner, Brian J. Williams, William I. Notz. 2nd ed. 2018. New York, NY : Springer New York : Imprint: Springer, 2018 XV, 436 p. 123 illus., 62 illus. in color. online forrás szöveg txt rdacontent számítógépes c rdamedia távoli hozzáférés cr rdacarrier szövegfájl PDF rda Springer Series in Statistics, 0172-7397 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. Nyomtatott kiadás: ISBN 9781493988457 Nyomtatott kiadás: ISBN 9781493988464 Az e-könyvek a teljes ELTE IP-tartományon belül online elérhetők. könyv e-book Mathematical statistics. EUL10000079757 Y Statistics. EUL10000081563 Y Engineering mathematics EUL10000420160 Y Statistical Theory and Methods. Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences. Mathematical and Computational Engineering. elektronikus könyv Williams, Brian J. szerző aut http://id.loc.gov/vocabulary/relators/aut Notz, William I. . szerző aut http://id.loc.gov/vocabulary/relators/aut SpringerLink (Online service) közreadó testület Online változat https://doi.org/10.1007/978-1-4939-8847-1 EUL01 |
| language |
English |
| format |
Book |
| author |
Santner, Thomas J. ., szerző |
| spellingShingle |
Santner, Thomas J. ., szerző The Design and Analysis of Computer Experiments Springer Series in Statistics,, ISSN 0172-7397 Mathematical statistics. Statistics. Engineering mathematics Statistical Theory and Methods. Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences. Mathematical and Computational Engineering. elektronikus könyv |
| author_facet |
Santner, Thomas J. ., szerző Williams, Brian J., szerző Notz, William I. ., szerző SpringerLink (Online service), közreadó testület |
| author2 |
Williams, Brian J., szerző Notz, William I. ., szerző |
| author_corporate |
SpringerLink (Online service), közreadó testület |
| author_sort |
Santner, Thomas J. . |
| title |
The Design and Analysis of Computer Experiments |
| title_short |
The Design and Analysis of Computer Experiments |
| title_full |
The Design and Analysis of Computer Experiments by Thomas J. Santner, Brian J. Williams, William I. Notz. |
| title_fullStr |
The Design and Analysis of Computer Experiments by Thomas J. Santner, Brian J. Williams, William I. Notz. |
| title_full_unstemmed |
The Design and Analysis of Computer Experiments by Thomas J. Santner, Brian J. Williams, William I. Notz. |
| title_auth |
The Design and Analysis of Computer Experiments |
| title_sort |
design and analysis of computer experiments |
| series |
Springer Series in Statistics,, ISSN 0172-7397 |
| series2 |
Springer Series in Statistics, |
| publishDate |
2018 |
| publishDateSort |
2018 |
| physical |
XV, 436 p. 123 illus., 62 illus. in color. : online forrás |
| edition |
2nd ed. 2018. |
| isbn |
978-1-4939-8847-1 |
| issn |
0172-7397 |
| callnumber-first |
Q - Science |
| callnumber-subject |
QA - Mathematics |
| callnumber-label |
QA276-280 |
| callnumber-raw |
978894 |
| callnumber-search |
978894 |
| topic |
Mathematical statistics. Statistics. Engineering mathematics Statistical Theory and Methods. Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences. Mathematical and Computational Engineering. elektronikus könyv |
| topic_facet |
Mathematical statistics. Statistics. Engineering mathematics Statistical Theory and Methods. Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences. Mathematical and Computational Engineering. elektronikus könyv Mathematical statistics. Statistics. Engineering mathematics Statistical Theory and Methods. Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences. Mathematical and Computational Engineering. |
| url |
https://doi.org/10.1007/978-1-4939-8847-1 |
| illustrated |
Not Illustrated |
| dewey-hundreds |
500 - Science |
| dewey-tens |
510 - Mathematics |
| dewey-ones |
519 - Probabilities & applied mathematics |
| dewey-full |
519.5 |
| dewey-sort |
3519.5 |
| dewey-raw |
519.5 |
| dewey-search |
519.5 |
| first_indexed |
2023-12-26T23:19:25Z |
| last_indexed |
2023-12-29T19:19:18Z |
| recordtype |
opac |
| publisher |
New York, NY : : Springer New York : : Imprint: Springer, |
| _version_ |
1786641341320527873 |
| score |
13,375745 |
| generalnotes |
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. |