| 期刊全称 | Bayesian Optimization with Application to Computer Experiments | | 影响因子2023 | Tony Pourmohamad,Herbert K. H. Lee | | 视频video | | | 发行地址 | 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 | | 学科分类 | SpringerBriefs in Statistics | | 图书封面 |  | | 影响因子 | .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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发表于 2025-3-21 17:00:53
