This book introduces readers to Bayesian optimization, highlighting
advances in the field and showcasing its successful applications to
computer experiments. R code is available as online supplementary
material for most included examples, so that readers can better
comprehend and reproduce methods.
Compact and accessible, the volume is broken down into four chapters.
Chapter 1 introduces the reader to the topic of computer experiments; it
includes a variety of examples across many industries. Chapter 2 focuses
on the task of surrogate model building and contains a mix of several
different surrogate models that are used in the computer modeling and
machine learning communities. Chapter 3 introduces the core concepts of
Bayesian optimization and discusses unconstrained optimization. Chapter
4 moves on to constrained optimization, and showcases some of the most
novel methods found in the field.
This will be a useful companion to researchers and practitioners working
with computer experiments and computer modeling. Additionally, readers
with a background in machine learning but minimal background in computer
experiments will find this book an interesting case study of the
applicability of Bayesian optimization outside the realm of machine
learning.