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Bayesian Optimization with Application to Computer Experiments (Paperback, 1st ed. 2021)
Loot Price: R1,865
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Bayesian Optimization with Application to Computer Experiments (Paperback, 1st ed. 2021)
Series: SpringerBriefs in Statistics
Expected to ship within 10 - 15 working days
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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.
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