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Table of Contents

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

About the Author

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.


"The coverage of this book is unique and important. It focuses on a current area at the edge of applied mathematics and statistics, a domain that really should be substantially better-developed. For researchers and students who already have a solid foundation in statistics and familiarity with R, and want to know more about how statistics can be used in the approximation of complex functions and numerical optimization (i.e. computer experiments), this should be a welcome resource."
-Max Morris, Iowa State University, USA

"This book is a fantastic exploration of Gaussian process surrogates and a variety of applications to which they have been utilized. This approach is rapidly expanding in both the statistical and machine learning communities. I particularly enjoyed the applied focus of this book and the ease with which the author enables the reader to "follow along", by providing code for each example discussed. In my view, the technical content of the book is well-chosen, and the flow of material should be very well-received by the readership."
-Brian J. Williams, Scientist, Los Alamos National Laboratory

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