Tony Pourmohamad

(Author)

Bayesian Optimization with Application to Computer Experiments (2021)Paperback - 2021, 5 October 2021

Bayesian Optimization with Application to Computer Experiments (2021)
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Part of Series
Springerbriefs in Statistics
Print Length
104 pages
Language
English
Publisher
Springer
Date Published
5 Oct 2021
ISBN-10
3030824578
ISBN-13
9783030824570

Description

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.

Product Details

Authors:
Tony PourmohamadHerbert K H Lee
Book Edition:
2021
Book Format:
Paperback
Country of Origin:
NL
Date Published:
5 October 2021
Dimensions:
23.39 x 15.6 x 0.61 cm
ISBN-10:
3030824578
ISBN-13:
9783030824570
Language:
English
Location:
Cham
Pages:
104
Publisher:
Weight:
172.36 gm

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