About Bayesian Optimization¶
Bayesian optimization is a global optimization strategy for black-box and expensive-to-evaluate functions. Generic Bayesian optimization follows these steps:
- Build a surrogate function with historical inputs and their observations.
- Compute and maximize an acquisition function, defined by the outputs of surrogate function.
- Observe the maximizer of acquisition function from a true objective function.
- Accumulate the maximizer and its observation.
This project helps us to execute this Bayesian optimization procedure. In particular, Gaussian process regression is used as a surrogate function, and various acquisition functions such as probability improvement, expected improvement, and Gaussian process upper confidence bound are included in this project.