@article{1440219,
      recid = {1440219},
      author = {Pourmohamad, Tony, and Lee, Herbert K. H.,},
      title = {Bayesian optimization with application to computer  experiments /},
      pages = {1 online resource :},
      abstract = {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.},
      url = {http://library.usi.edu/record/1440219},
      doi = {https://doi.org/10.1007/978-3-030-82458-7},
}