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, 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.
Uncertainty quantification, sensitivity analysis, calibration, sequential design/active learning and (blackbox/Bayesian) optimization.
Advanced topics: treed partitioning, local GP approximation, coupled nonlinear mean and variance (heteroskedastic) models.
Treatment appreciates historical response surface methodology (RSM), 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.
还没人写过短评呢