Computer Science
贝叶斯优化
Bayesian optimizationA sequential design strategy for global optimization of black-box functions that are expensive to evaluate. It uses a probabilistic model (Gaussian process) to predict the performance of untested points and an acquisition function to decide the next point to evaluate. / 一种用于昂贵黑盒函数全局优化的序列设计策略。它使用概率模型（高斯过程）来预测未测试点的性能，并使用获取函数来决定下一个要评估的点。
- Knowledge type
- 数学理论
- First proposed or discovered
- Proposer
- Turkey Farm member
- Creator
- 农场主
- Last updated
- 2026-11-10 19:07:01
Sub-knowledge points
Surrogate modelingAcquisition function maximizationIterative sampling
Higher-order knowledge points
Main parameters and performance indicators
Acquisition function: Determines the next point to sample (e.g., Expected Improvement). / 获取函数：决定下一个采样点（例如，期望改进）。Gaussian Process: The probabilistic model used to model the objective function. / 高斯过程：用于建模目标函数的概率模型。
Open problems
Scalability to high-dimensional problems. / 高维问题的可扩展性。Handling noisy observations. / 处理噪声观测值。
Related literature
ranked by community starsBayesian optimization-guided closed-loop morphology optimization of block copolymer blends via synchrotron X-ray scatteringOpen source
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1 recordsRecord title: Bayesian optimization.
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