Bayesian Optimization

Preface
Notation
Introduction
Gaussian Processes
Modeling with Gaussian processes
Model assessment, selection, and averaging
Decision Theory for Optimization
Utility Functions for Optimization
Common Bayesian Optimization Policies
Computing Policies with Gaussian Processes
Implementation
Theoretical analysis
Extensions and related settings
A Brief History of Bayesian Optimization
The Gaussian distribution
Methods for approximate Bayesian inference
Gradients
Annotated bibliography of applications
Bibliography
Index