Components of Bayesian Optimization

Bayesian optimization is a powerful optimization technique that is particularly effective when the objective function is expensive to evaluate or noisy. It combines the concepts of Bayesian inference and optimization to efficiently explore and exploit the search space in order to find the optimal solution. Bayesian optimization maintains a probabilistic model of the objective function and uses it to guide the search toward promising regions. It iteratively suggests new candidate solutions to evaluate based on the information gained from previous evaluations. In this detailed overview, we'll explore the different components of Bayesian optimization and how they work together to solve optimization problems effectively.

The components of Bayesian optimization are shown below: