方法对比

	贝叶斯高斯过程	贝叶斯优化	高斯过程
领域	机器学习	优化	机器学习
方法族	Machine learning	Process / pipeline	Machine learning
起源年份	1978–2006	1975 (foundational); 2012 (ML standard)	2006 (book); roots in Kriging, 1951)
提出者	O'Hagan, A.; Neal, R. M.; Rasmussen, C. E. & Williams, C. K. I.	Mockus (1975); popularised for ML by Snoek, Larochelle & Adams (2012)	Rasmussen, C. E. & Williams, C. K. I.
类型	Probabilistic kernel model	Sequential model-based black-box optimization	Probabilistic non-parametric model
开创性文献	Rasmussen, C. E., & Williams, C. K. I. (2006). Gaussian Processes for Machine Learning. MIT Press. ISBN: 978-0-262-18253-9	Snoek, J., Larochelle, H., & Adams, R.P. (2012). Practical Bayesian Optimization of Machine Learning Algorithms. Advances in Neural Information Processing Systems (NeurIPS), 25.	Rasmussen, C. E., & Williams, C. K. I. (2006). Gaussian Processes for Machine Learning. MIT Press. ISBN: 978-0-262-18253-9
别名	GP regression, GPR, Gaussian process model, GP classifier	Bayesçi Optimizasyon (Hyperparameter Tuning), surrogate-based optimization, sequential model-based optimization, SMBO	GP, Gaussian Process Regression, GPR, Kriging
相关	3	2	3
摘要	A Bayesian Gaussian Process (GP) places a probability distribution directly over functions, using a kernel to encode similarity between inputs. After observing data, Bayes' rule converts this prior into a posterior that yields not just point predictions but calibrated uncertainty estimates at every new input — making it one of the most principled probabilistic models in machine learning.	Bayesian Optimization is a sequential, model-based strategy for finding the optimum of expensive black-box functions with as few evaluations as possible. Rooted in the work of Mockus (1975) and brought to mainstream machine-learning practice by Snoek, Larochelle, and Adams (2012), it fits a probabilistic surrogate model — typically a Gaussian Process — to past observations and uses an acquisition function to decide where to probe next, balancing exploration of unknown regions with exploitation of promising ones.	A Gaussian Process (GP) is a non-parametric, fully probabilistic machine learning model that places a prior distribution directly over functions. Rather than predicting a single value, it returns a predictive mean and a calibrated uncertainty estimate at every test point, making it especially valuable for regression on small to medium datasets and for Bayesian optimization tasks.