In Bayesian Optimization, the process begins by selecting a prior distribution for the objective function, often modeled as a Gaussian process. As evaluations are conducted, the model updates to reflect the observed outcomes, providing a measure of uncertainty around the predictions. This balance between exploration (trying new parameters) and exploitation (refining known good parameters) allows Bayesian Optimization to efficiently converge on optimal solutions, particularly in scenarios where evaluations are costly or time-consuming.
Bayesian Optimization Example
For example, a tech company might use Bayesian Optimization to tune hyperparameters in a machine learning model. Instead of evaluating every combination of hyperparameters, which can be computationally expensive, the company can use Bayesian Optimization to intelligently select which combinations to test next based on past results, ultimately leading to a more efficient search for the best model performance.