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portada Surrogates: Gaussian Process Modeling, Design, and Optimization for the Applied Sciences (Chapman & Hall
Type
Physical Book
Publisher
Author
Year
2020
Language
English
Pages
559
Format
Hardcover
ISBN13
9780367415426
Edition No.
1
Categories

Surrogates: Gaussian Process Modeling, Design, and Optimization for the Applied Sciences (Chapman & Hall

Robert B. Gramacy (Author) · Crc Pr Inc · Hardcover

Surrogates: Gaussian Process Modeling, Design, and Optimization for the Applied Sciences (Chapman & Hall - Robert B. Gramacy

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Synopsis "Surrogates: Gaussian Process Modeling, Design, and Optimization for the Applied Sciences (Chapman & Hall "

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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