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
8 Calibration and Sensitivity
9 GP Fidelity and Scale
Appendix A Numerical Linear Algebra for Fast GPs
Appendix B An Experiment Game
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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