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Author:
Gramacy, Robert B. author.
Title:
Surrogates : Gaussian process modeling, design, and optimization for the applied sciences / Robert B. Gramacy.
Publisher:
CRC PressTaylor & Francis Group,
Copyright Date:
2020
Description:
xv, 543 pages : illustrations ; 27 cm.
Subject:
Gaussian processes--Data processing.
Regression analysis--Mathematical models.
Response surfaces (Statistics)
R (Computer program language)
Computer simulation.
Computer simulation.
Gaussian processes--Data processing.
R (Computer program language)
Regression analysis--Mathematical models.
Response surfaces (Statistics)
Notes:
Includes bibliographical references and index.
Contents:
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.
Summary:
"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"-- Provided by publisher.
Series:
Chapman & Hall/CRC texts in statistical science series
ISBN:
0367415429
9780367415426
OCLC:
(OCoLC)1120696204
LCCN:
2019042570
Locations:
USUX851 -- Iowa State University - Parks Library (Ames)

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This resource is supported by the Institute of Museum and Library Services under the provisions of the Library Services and Technology Act as administered by State Library of Iowa.