Bayesian Optimization with Application to Computer Experiments by Tony Pourmoham

Description: Bayesian Optimization with Application to Computer Experiments by Tony Pourmohamad, Herbert K.H. Lee This book introduces readers to Bayesian optimization, highlighting advances in the field and showcasing its successful applications to computer experiments. FORMAT Paperback LANGUAGE English CONDITION Brand New Publisher 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. Back Cover 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. Author Biography Tony Pourmohamad is a principal statistical scientist in the Department of Biostatistics at Genentech. Prior to joining Genentech, he received his Ph.D. from the Department of Statistics and Applied Mathematics at the University of California, Santa Cruz, where his research focused on constrained optimization for computer experiments. Nowadays, he spends most of his time at the intersection of clinical and nonclinical statistics at Genentech.Herbert Lee is Professor of Statistics in the Baskin School of Engineering at the University of California, Santa Cruz. He currently also serves as Vice Provost for Academic Affairs. He received his Ph.D. from the Department of Statistics at Carnegie Mellon University and completed a postdoc at Duke University. His research interests include Bayesian statistics, computer simulation experiments, inverse problems, and spatial statistics. Table of Contents 1. Computer experiments.- 2. Surrogate models.- 3. Unconstrained optimization.- 4. Constrained optimization. Feature 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 experiments Details ISBN3030824578 Author Herbert K.H. Lee Series SpringerBriefs in Statistics Language English Year 2021 ISBN-10 3030824578 ISBN-13 9783030824570 Format Paperback DOI 10.1007/978-3-030-82458-7 Publisher Springer Nature Switzerland AG Edition 1st Imprint Springer Nature Switzerland AG Place of Publication Cham Country of Publication Switzerland Pages 104 Publication Date 2021-10-05 UK Release Date 2021-10-05 Edited by Steven Furnell Birth 1927 Affiliation Massachusetts Institute of Technology Position journalist Qualifications S. J. Edition Description 1st ed. 2021 DEWEY 519.542 Audience Professional & Vocational Illustrations 56 Illustrations, color; 8 Illustrations, black and white; X, 104 p. 64 illus., 56 illus. in color. We've got this At The Nile, if you're looking for it, we've got it. With fast shipping, low prices, friendly service and well over a million items - you're bound to find what you want, at a price you'll love! TheNile_Item_ID:134250677;

Price: 132.07 AUD

Location: Melbourne

End Time: 2024-12-28T02:09:44.000Z

Shipping Cost: 9.35 AUD