TY  - BOOK
AU  - Gramacy,Robert B.
TI  - Surrogates: Gaussian process modeling, design, and optimization for the applied sciences 
T2  - Chapman & Hall/CRC texts in statistical science series
SN  - 9780367815493
AV  - QA274.4 .G73 2020
U1  - 519.8/2 23
PY  - 2020///]
CY  - Boca Raton, FL
PB  - CRC Press
KW  - Gaussian processes
KW  - Data processing
KW  - Regression analysis
KW  - Mathematical models
KW  - Response surfaces (Statistics)
KW  - R (Computer program language)
KW  - Computer simulation
KW  - MATHEMATICS / Probability & Statistics / Regression Analysis
KW  - bisacsh
KW  - MATHEMATICS / Probability & Statistics / Multivariate Analysis
N1  - "A Chapman & Hall Book" -- Title page."; 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
N2  - "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"--
UR  - https://www.taylorfrancis.com/books/9780367815493
UR  - http://www.oclc.org/content/dam/oclc/forms/terms/vbrl-201703.pdf
ER  -