SpringerBriefs in Statistics
cover

Bayesian Optimization with Application to Computer Experiments

Authors: Pourmohamad, Tony, Lee, Herbert

  • Features accompanying R code for most included examples
  • Addresses readers seeking detailed explanations of methodology
  • Unique in its discussion of the application of Bayesian optimization to computer experiments
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eBook 46,00 €
price for Spain (gross)
  • The eBook version of this title will be available soon
  • Due: October 30, 2021
  • ISBN 978-3-030-82458-7
  • Digitally watermarked, DRM-free
  • Included format:
  • ebooks can be used on all reading devices
Softcover 57,19 €
price for Spain (gross)
  • Due: October 30, 2021
  • ISBN 978-3-030-82457-0
  • Free shipping for individuals worldwide
  • Institutional customers should get in touch with their account manager
  • Covid-19 shipping restrictions
  • The final prices may differ from the prices shown due to specifics of VAT rules
About this book

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.          

About the authors

Tony Pourmohamad is a principal statistical scientist in the Department of Biostatistics at Genentech. Prior to joining Genentech, he received his Ph.D. from the Department of Statistics and Applied Mathematics at the University of California, Santa Cruz, where his research focused on constrained optimization for computer experiments. Nowadays, he spends most of his time at the intersection of clinical and nonclinical statistics at Genentech.

Herbert Lee is Professor of Statistics in the Baskin School of Engineering at the University of California, Santa Cruz. He currently also serves as Vice Provost for Academic Affairs. He received his Ph.D. from the Department of Statistics at Carnegie Mellon University and completed a postdoc at Duke University. His research interests include Bayesian statistics, computer simulation experiments, inverse problems, and spatial statistics.

Buy this book

eBook 46,00 €
price for Spain (gross)
  • The eBook version of this title will be available soon
  • Due: October 30, 2021
  • ISBN 978-3-030-82458-7
  • Digitally watermarked, DRM-free
  • Included format:
  • ebooks can be used on all reading devices
Softcover 57,19 €
price for Spain (gross)
  • Due: October 30, 2021
  • ISBN 978-3-030-82457-0
  • Free shipping for individuals worldwide
  • Institutional customers should get in touch with their account manager
  • Covid-19 shipping restrictions
  • The final prices may differ from the prices shown due to specifics of VAT rules
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Bibliographic Information

Bibliographic Information
Book Title
Bayesian Optimization with Application to Computer Experiments
Authors
Series Title
SpringerBriefs in Statistics
Copyright
2021
Publisher
Springer International Publishing
Copyright Holder
The Author(s), under exclusive license to Springer Nature Switzerland AG
eBook ISBN
978-3-030-82458-7
DOI
10.1007/978-3-030-82458-7
Softcover ISBN
978-3-030-82457-0
Series ISSN
2191-544X
Edition Number
1
Number of Pages
X, 104
Number of Illustrations
8 b/w illustrations, 56 illustrations in colour
Topics