000 04077cam a2200565 i 4500
001 9780367815493
003 FlBoTFG
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006 m d | |
007 cr |||||||||||
008 191229s2020 flua ob 001 0 eng
040 _aOCoLC-P
_beng
_erda
_cOCoLC-P
020 _a9780367815493
_qelectronic book
020 _a0367815494
_qelectronic book
020 _a9781000766523
_qelectronic book
020 _a1000766527
_qelectronic book
020 _a9781000766202
_qelectronic book
020 _a1000766209
_qelectronic book
020 _a9781000766363
_qelectronic book
020 _a1000766365
_qelectronic book
020 _z9780367415426
_qhardcover
020 _z0367415429
_qhardcover
035 _a(OCoLC)1136417827
035 _a(OCoLC-P)1136417827
050 0 4 _aQA274.4
_b.G73 2020
072 7 _aMAT
_x029030
_2bisacsh
072 7 _aMAT
_x029020
_2bisacsh
072 7 _aPBT
_2bicssc
082 0 0 _a519.8/2
_223
100 1 _aGramacy, Robert B.,
_eauthor.
245 1 0 _aSurrogates :
_bGaussian process modeling, design, and optimization for the applied sciences /
_cRobert B. Gramacy.
264 1 _aBoca Raton, FL :
_bCRC Press,
_c[2020]
300 _a1 online resource (xv, 543 pages)
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bn
_2rdamedia
338 _aonline resource
_bnc
_2rdacarrier
490 1 _aChapman & Hall/CRC texts in statistical science series
500 _a"A Chapman & Hall Book" -- Title page."
505 0 _aHistorical 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"--
_cProvided by publisher.
588 _aOCLC-licensed vendor bibliographic record.
650 0 _aGaussian processes
_xData processing.
650 0 _aRegression analysis
_xMathematical models.
650 0 _aResponse surfaces (Statistics)
650 0 _aR (Computer program language)
650 0 _aComputer simulation.
650 7 _aMATHEMATICS / Probability & Statistics / Regression Analysis
_2bisacsh
650 7 _aMATHEMATICS / Probability & Statistics / Multivariate Analysis
_2bisacsh
856 4 0 _3Taylor & Francis
_uhttps://www.taylorfrancis.com/books/9780367815493
856 4 2 _3OCLC metadata license agreement
_uhttp://www.oclc.org/content/dam/oclc/forms/terms/vbrl-201703.pdf
999 _c127183
_d127183