Téléchargez le livre : Bayesian Optimization with Application to Computer Experiments

Bayesian Optimization with Application to Computer Experiments

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Éditeur :

Springer


Collection :

SpringerBriefs in Statistics

Paru le : 2021-10-04

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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 workingwith 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.          
Pages
104 pages
Collection
SpringerBriefs in Statistics
Parution
2021-10-04
Marque
Springer
EAN papier
9783030824570
EAN PDF
9783030824587
Informations sur l'ebook
Nombre pages copiables
1
Nombre pages imprimables
10
Taille du fichier
8550 Ko
Prix
68,56 €
EAN EPUB
9783030824587
Informations sur l'ebook
Nombre pages copiables
1
Nombre pages imprimables
10
Taille du fichier
21299 Ko
Prix
68,56 €

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