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Titlebook: Bayesian Optimization with Application to Computer Experiments; Tony Pourmohamad,Herbert K. H. Lee Book 2021 The Author(s), under exclusiv

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發(fā)表于 2025-3-21 17:00:53 | 只看該作者 |倒序瀏覽 |閱讀模式
期刊全稱Bayesian Optimization with Application to Computer Experiments
影響因子2023Tony Pourmohamad,Herbert K. H. Lee
視頻videohttp://file.papertrans.cn/182/181875/181875.mp4
發(fā)行地址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
學(xué)科分類SpringerBriefs in Statistics
圖書封面Titlebook: Bayesian Optimization with Application to Computer Experiments;  Tony Pourmohamad,Herbert K. H. Lee Book 2021 The Author(s), under exclusiv
影響因子.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 applicabilit
Pindex Book 2021
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Surrogate Models, primary focus is on Gaussian processes, which are used throughout the book. Simple examples demonstrate various aspects of the models. Treed Gaussian processes and radial basis functions are presented as possible alternatives.
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Constrained Optimization,raints. Different acquisition functions are needed, and four examples are explored in more detail: constrained expected improvement, asymmetric entropy, augmented Lagrangian, and barrier methods. These four are demonstrated on both simple and more complex examples.
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The Changing Order in the World of Workraints. Different acquisition functions are needed, and four examples are explored in more detail: constrained expected improvement, asymmetric entropy, augmented Lagrangian, and barrier methods. These four are demonstrated on both simple and more complex examples.
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Computer Experiments,This chapter introduces the book with several examples of computer models and associated optimization problems. It also provides an introduction to space-filling designs, with a focus on Latin hypercube designs.
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