Bayesian Optimization with Application to Computer Experiments (SpringerBriefs in Statistics)

✍ Scribed by Tony Pourmohamad

• Publisher: Springer
• Year: 2021
• Tongue: English
• Leaves: 113
• Edition: 1st ed. 2021
• Category: Library

✦ Synopsis

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.

✦ Table of Contents

Preface
Contents
1 Computer Experiments
1.1 Introduction
1.2 Examples of Computer Experiments
1.2.1 Groundwater Remediation
1.2.2 Cosmology
1.2.3 Drug Discovery
1.2.4 Garden Sprinkler
1.3 Space-Filling Designs
2 Surrogate Models
2.1 Gaussian Processes
2.2 Treed Gaussian Processes
2.3 Radial Basis Functions
3 Unconstrained Optimization
3.1 Bayesian Optimization
3.2 The Role of the Acquisition Function
3.3 Choice of Acquisition Function
3.3.1 Probability of Improvement
3.3.2 Expected Improvement
3.3.3 Lower Confidence Bound
3.3.4 Other Acquisition Functions
Random Search
Thompson Sampling
Entropy Search
3.4 Sprinkler Computer Model
4 Constrained Optimization
4.1 Constrained Bayesian Optimization
4.2 Choice of Acquisition Function
4.2.1 Constrained Expected Improvement
4.2.2 Asymmetric Entropy
4.2.3 Augmented Lagrangian
4.2.4 Barrier Methods
4.3 Constrained Sprinkler Computer Model
5 Conclusions
A R Code
A.1 Getting Started
References