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Bayesian optimization
Sequential model-based optimization of expensive black-box functions
Bayesian optimization
Bayesian optimization is a sequential model-based strategy for global optimization of black-box objective functions whose evaluations are costly. It is commonly used when a single observation requires an experiment, engineering computation, numerical simulation, or machine-learning run, and when derivatives are unavailable or unreliable. The objective need not have a closed-form expression. The method constructs a probabilistic model of the unknown function, often a Gaussian process (GP), and uses the resulting predictive distribution to choose the next evaluation point. This choice is made by optimizing a sampling criterion, also called an acquisition function. Common applications include hyperparameter optimization in machine learning, where each trial may require training and validating a model, and engineering design problems driven by expensive numerical simulations.
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