Bayesian Optimization

Bayesian Optimization is a global optimization method based on Bayesian statistics, designed to efficiently find the optimal solution by constructing a probabilistic model of the objective function. This method is particularly suitable for optimizing black-box functions where evaluations are expensive and derivatives are unavailable. Its core lies in continuously updating the posterior distribution using prior knowledge and observed data, thereby guiding the selection of subsequent sampling points to minimize the number of searches required to achieve the optimization goal. Bayesian Optimization has significant application value in areas such as hyperparameter tuning in machine learning, experimental design, and engineering optimization.