杭州电子科技大学图书馆书目检索系统

Online Public Access Catalogue

题名/责任者:

Surrogates : Gaussian process modeling, design, and optimization for the applied sciences / [Robert B. Gramacy, author].

出版发行项:

New York, NY : CRC Press ; Taylor & Francis Group, [2020]

ISBN:

9780367415426

载体形态项:

xv, 543 pages : illustrations ; 26 cm

个人责任者:

Gramacy, Robert B., author.

论题主题:

Gaussian processes-Data processing.

论题主题:

Regression analysis-Mathematical models.

论题主题:

Response surfaces (Statistics)

论题主题:

R (Computer program language)

论题主题:

Computer simulation.

中图法分类号:

O211.6

书目附注:

Includes bibliographical references and index.

内容附注:

Historical perspective -- Four motivating datasets -- Steepest ascent and ridge analysis -- Space-filling design -- Gaussian process regression -- Model-based design for GPs -- Optimization -- Calibration and sensitivity -- GP fidelity and scale -- Heteroskedasticity.

摘要附注:

"Surrogates: a graduate textbook, or professional handbook, on topics at the interface between machine learning, spatial statistics, computer simulation, meta-modeling (i.e., emulation), design of experiments, and optimization. Experimentation through simulation, "human out-of-the-loop" statistical support (focusing on the science), management of dynamic processes, online and real-time analysis, automation, and practical application are at the forefront. Topics include: Gaussian process (GP) regression for flexible nonparametric and nonlinear modeling. Applications to uncertainty quantification, sensitivity analysis, calibration of computer models to field data, sequential design/active learning and (blackbox/Bayesian) optimization under uncertainty. Advanced topics include treed partitioning, local GP approximation, modeling of simulation experiments (e.g., agent-based models) with coupled nonlinear mean and variance (heteroskedastic) models. Treatment appreciates historical response surface methodology (RSM) and canonical examples, but emphasizes contemporary methods and implementation in R at modern scale. Rmarkdown facilitates a fully reproducible tour, complete with motivation from, application to, and illustration with, compelling real-data examples. Presentation targets numerically competent practitioners in engineering, physical, and biological sciences. Writing is statistical in form, but the subjects are not about statistics. Rather, they're about prediction and synthesis under uncertainty; about visualization and information, design and decision making, computing and clean code"--

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