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  2. Sandsynlighedsregning og statistik
  3. Bayesiansk statistik

Bayesian Optimization with Application to Computer Experiments

Af: Herbert K. H. Lee, Tony Pourmohamad Engelsk Paperback

Bayesian Optimization with Application to Computer Experiments

Af: Herbert K. H. Lee, Tony Pourmohamad Engelsk Paperback
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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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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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Produktdetaljer
Sprog: Engelsk
Sider: 104
ISBN-13: 9783030824570
Indbinding: Paperback
Udgave:
ISBN-10: 3030824578
Kategori: Bayesiansk statistik
Udg. Dato: 5 okt 2021
Længde: 0mm
Bredde: 155mm
Højde: 235mm
Forlag: Springer Nature Switzerland AG
Oplagsdato: 5 okt 2021
Forfatter(e): Herbert K. H. Lee, Tony Pourmohamad
Forfatter(e) Herbert K. H. Lee, Tony Pourmohamad

Kategori Bayesiansk statistik

ISBN-13 9783030824570

Sprog Engelsk

Indbinding Paperback

Sider 104

Udgave

Længde 0mm

Bredde 155mm

Højde 235mm

Udg. Dato 5 okt 2021

Oplagsdato 5 okt 2021

Forlag Springer Nature Switzerland AG

Kategori sammenhænge