Companion webpage for the book *Bayesian Optimization* by Roman Garnett

Copyright 2021 Roman Garnett, to be published by Cambridge University Press

This is a (draft) monograph on Bayesian optimization. The book aims to provide a self-contained and comprehensive introduction to Bayesian optimization, starting “from scratch” and carefully developing all the key ideas along the way. The intended audience is graduate students and researchers in machine learning, statistics, and related fields. However, I also hope that practitioners and researchers from more distant fields will find some utility here.

The book is divided into three main parts, covering:

- theoretical and practical aspects of Gaussian process modeling,
- the Bayesian approach to sequential decision making, and
- the realization of practical and effective optimization policies.

A few additional topics are also covered:

- an overview of theoretical convergence results,
- a survey of notable extensions,
- a comprehensive history of Bayesian optimization, and
- an annotated bibliography of applications.

The book is in the final stages of preparation. I am making the draft available for initial commentary before publication. Once published, the book will remain freely available on the companion webpage.

I welcome feedback on the manuscript! Please feel free to file an issue.