9781493988457 - the design and analysis of computer experiments (springer series in statistics) de santner (19 resultados)
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Serie: Springer Series in Statistics, Libro 152 de 160. Libro 152 de 160 - Springer Series in Statistics
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Editorial: Springer 2019
Serie: Springer Series in Statistics, Libro 152 de 160. Libro 152 de 160 - Springer Series in Statistics
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Editorial: Springer 2019
Serie: Springer Series in Statistics, Libro 152 de 160. Libro 152 de 160 - Springer Series in Statistics
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Editorial: Springer 2019
Serie: Springer Series in Statistics, Libro 152 de 160. Libro 152 de 160 - Springer Series in Statistics
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Serie: Springer Series in Statistics, Libro 152 de 160. Libro 152 de 160 - Springer Series in Statistics
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Serie: Springer Series in Statistics, Libro 152 de 160. Libro 152 de 160 - Springer Series in Statistics
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Hardback. Condición: New. Second Edition 2018. 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 experime…nts. 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.
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Editorial: Springer 2019
Serie: Springer Series in Statistics, Libro 152 de 160. Libro 152 de 160 - Springer Series in Statistics
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Editorial: Springer 2019
Serie: Springer Series in Statistics, Libro 152 de 160. Libro 152 de 160 - Springer Series in Statistics
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Hardback. Condición: New. Second Edition 2018. 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 experime…nts. 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.
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Editorial: Springer, Humana 2019
Serie: Springer Series in Statistics, Libro 152 de 160. Libro 152 de 160 - Springer Series in Statistics
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Buch. Condición: Neu. Druck auf Anfrage Neuware - Printed after ordering - 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 a…djuncts 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.
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Editorial: Springer Verlag 2019
Serie: Springer Series in Statistics, Libro 152 de 160. Libro 152 de 160 - Springer Series in Statistics
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Hardcover. Condición: Brand New. 2nd edition. 436 pages. 9.25x6.25x1.25 inches. In Stock.
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Editorial: Springer 2019
Serie: Springer Series in Statistics, Libro 152 de 160. Libro 152 de 160 - Springer Series in Statistics
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Hardback. Condición: New. This item is printed on demand. New copy - Usually dispatched within 5-9 working days.
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Serie: Springer Series in Statistics, Libro 152 de 160. Libro 152 de 160 - Springer Series in Statistics
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Gebunden. Condición: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. New to this revised and expanded edition:An expanded presentation of basic material on computer experiments and Gaussian processes with additional simulations and examplesA new comparison of plug-.
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Editorial: Springer, Humana Jan 2019 2019
Serie: Springer Series in Statistics, Libro 152 de 160. Libro 152 de 160 - Springer Series in Statistics
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Buch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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 surr…ogates 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 452 pp. Englisch.
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Editorial: Springer, Humana Jan 2019 2019
Serie: Springer Series in Statistics, Libro 152 de 160. Libro 152 de 160 - Springer Series in Statistics
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Buch. Condición: Neu. This item is printed on demand - Print on Demand Titel. Neuware -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 surrogat…es 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 practitionersSpringer-Verlag KG, Sachsenplatz 4-6, 1201 Wien 452 pp. Englisch.
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Editorial: Springer 2019
Serie: Springer Series in Statistics, Libro 152 de 160. Libro 152 de 160 - Springer Series in Statistics
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