04111cam a2200577 i 4500 9780367815493 FlBoTFG 20260210180730.0 m d | | cr ||||||||||| 191229s2020 flua ob 001 0 eng OCoLC-P eng rda OCoLC-P 9780367815493 electronic book 0367815494 electronic book 9781000766523 electronic book 1000766527 electronic book 9781000766202 electronic book 1000766209 electronic book 9781000766363 electronic book 1000766365 electronic book 9780367415426 hardcover 0367415429 hardcover (OCoLC)1136417827 (OCoLC-P)1136417827 QA274.4 .G73 2020 MAT 029030 bisacsh MAT 029020 bisacsh PBT bicssc 519.8/2 23 Gramacy, Robert B., author. Surrogates : Gaussian process modeling, design, and optimization for the applied sciences / Robert B. Gramacy. Boca Raton, FL : CRC Press, [2020] 1 online resource (xv, 543 pages) text txt rdacontent computer n rdamedia online resource nc rdacarrier Chapman & Hall/CRC texts in statistical science series "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. "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. OCLC-licensed vendor bibliographic record. Gaussian processes Data processing. Regression analysis Mathematical models. Response surfaces (Statistics) R (Computer program language) Computer simulation. 786 MATHEMATICS / Probability & Statistics / Regression Analysis bisacsh MATHEMATICS / Probability & Statistics / Multivariate Analysis bisacsh Taylor & Francis https://www.taylorfrancis.com/books/9780367815493 OCLC metadata license agreement http://www.oclc.org/content/dam/oclc/forms/terms/vbrl-201703.pdf 89732 89731