方法对比

	Bagging（Bootstrap Aggregating）	贝叶斯模型平均 (Bayesian Model Averaging, BMA)	高斯过程
领域	机器学习	贝叶斯	机器学习
方法族	Machine learning	Bayesian methods	Machine learning
起源年份	1996	1999	2006 (book); roots in Kriging, 1951)
提出者	Breiman, L.	Hoeting, Madigan, Raftery & Volinsky	Rasmussen, C. E. & Williams, C. K. I.
类型	Ensemble meta-algorithm (variance reduction via bootstrap aggregation)	Bayesian model averaging	Probabilistic non-parametric model
开创性文献	Breiman, L. (1996). Bagging Predictors. Machine Learning, 24(2), 123–140.	Hoeting, J. A., Madigan, D., Raftery, A. E. & Volinsky, C. T. (1999). Bayesian Model Averaging: A Tutorial. Statistical Science, 14(4), 382–401.	Rasmussen, C. E., & Williams, C. K. I. (2006). Gaussian Processes for Machine Learning. MIT Press. ISBN: 978-0-262-18253-9
别名	Bootstrap Aggregating, bootstrap aggregation, bagged ensemble, bagged predictor	BMA, Bayesian model combination, Bayesian Model Ortalaması (BMA)	GP, Gaussian Process Regression, GPR, Kriging
相关	5	5	3
摘要	Bagging, short for Bootstrap Aggregating, is an ensemble meta-algorithm introduced by Leo Breiman in 1996 that trains multiple copies of a base learner on independently drawn bootstrap samples of the training data and combines their predictions — by averaging for regression or majority vote for classification — to produce a final predictor with substantially lower variance than any single base learner.	Bayesian Model Averaging (BMA), formalised as a tutorial by Hoeting, Madigan, Raftery and Volinsky in 1999, addresses model uncertainty by averaging over all plausible model specifications rather than selecting a single best model. Each candidate model receives a posterior probability that reflects how well it fits the data given a prior, and predictions or coefficient estimates are formed as weighted averages across the entire model space. This approach reduces the bias and overconfidence that arise when a single selected model is treated as the true one.	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.