Bayesian optimization is a sequential design strategy for optimizing expensive black-box functions. It is particularly useful in scenarios where the objective function is expensive to evaluate, and obtaining data points is costly. This method leverages Bayesian statistics to model the objective function and make informed decisions about where to sample next.
