Bayesian Optimisation

Bayesian optimization is a principled and efficient global optimization technique suitable for optimizing costly black-box functions. This method sets a prior distribution over the objective function and updates this prior with the results of expensive function evaluations to form a posterior predictive distribution. The posterior distribution, combined with an acquisition function, guides the next observation location, balancing the exploitation of known high-performance regions with the exploration of areas with unknown information, thereby achieving the optimization goal. Bayesian optimization has significant application value in machine learning hyperparameter tuning, robotics, and engineering design.