机读格式显示(MARC)
- 000 03096cam a2200349 i 4500
- 008 191229s2020 nyua b 001 0 eng
- 020 __ |a 9780367415426 |c CNY822.60
- 040 __ |a LBSOR/DLC |b eng |c DLC |e rda
- 050 00 |a QA274.4 |b .G73 2020
- 100 1_ |a Gramacy, Robert B., |e author.
- 245 10 |a Surrogates : |b Gaussian process modeling, design, and optimization for the applied sciences / |c [Robert B. Gramacy, author].
- 260 __ |a New York, NY : |b CRC Press ; |b Taylor & Francis Group, |c [2020]
- 300 __ |a xv, 543 pages : |b illustrations ; |c 26 cm
- 336 __ |a text |b txt |2 rdacontent
- 337 __ |a unmediated |b n |2 rdamedia
- 338 __ |a volume |b nc |2 rdacarrier
- 504 __ |a Includes bibliographical references and index.
- 505 0_ |a 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.
- 520 __ |a "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"-- |c Provided by publisher.
- 650 _0 |a Gaussian processes |x Data processing.
- 650 _0 |a Regression analysis |x Mathematical models.
- 650 _0 |a Response surfaces (Statistics)
- 650 _0 |a R (Computer program language)
- 650 _0 |a Computer simulation.