方法对比

	高斯过程	贝叶斯高斯过程	随机森林
领域	机器学习	机器学习	机器学习
方法族	Machine learning	Machine learning	Machine learning
起源年份	2006 (book); roots in Kriging, 1951)	1978–2006	2001
提出者	Rasmussen, C. E. & Williams, C. K. I.	O'Hagan, A.; Neal, R. M.; Rasmussen, C. E. & Williams, C. K. I.	Breiman, L.
类型	Probabilistic non-parametric model	Probabilistic kernel model	Ensemble (bagging of decision trees)
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	Rasmussen, C. E., & Williams, C. K. I. (2006). Gaussian Processes for Machine Learning. MIT Press. ISBN: 978-0-262-18253-9	Breiman, L. (2001). Random Forests. Machine Learning, 45, 5–32.
别名	GP, Gaussian Process Regression, GPR, Kriging	GP regression, GPR, Gaussian process model, GP classifier	Rastgele Orman (Random Forest), rastgele orman, random decision forest, bagged tree ensemble
相关	3	3	4
摘要	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.	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.	Random Forest is an ensemble learning method, introduced by Leo Breiman in 2001, that grows many decision trees on bootstrap samples of the data and combines their votes to produce strong classification and regression. By pooling many slightly different trees, it produces more accurate and more stable predictions than any single tree.