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Surrogates is a graduate textbook, on topics at the interface between machine learning,spatial statistics,computer simulation,meta-modeling,design of experiments,and optimization. Experimentation through simulation,management of dynamic processes,online and real-time analysis,automation and practical application are at the forefront.
Inhaltsverzeichnis
1 Historical Perspective
2 Four Motivating Datasets
3 Steepest Ascent and Ridge Analysis
4 Space-filling Design
5 Gaussian process regression
6 Model-Based Design for GPs
7 Optimization
8 Calibration and Sensitivity
9 GP Fidelity and Scale
10 Heteroskedasticity
Appendix A Numerical Linear Algebra for Fast GPs
Appendix B An Experiment Game
Über den Autor / die Autorin
Robert B. Gramacy is a professor of Statistics in the College of Science at Virginia Tech. Research interests include Bayesian modeling methodology, statistical computing, Monte Carlo inference, nonparametric regression, sequential design, and optimization under uncertainty. Bobby enjoys cycling and ice hockey, and watching his kids grow up too fast.
Zusammenfassung
Surrogates is a graduate textbook, on topics at the interface between machine learning,spatial statistics,computer simulation,meta-modeling,design of experiments,and optimization. Experimentation through simulation,management of dynamic processes,online and real-time analysis,automation and practical application are at the forefront.
