So sánh phương pháp

	Tập hợp xếp chồng Bayes	Bagging (Bootstrap Aggregating)	Quá trình Gauss
Lĩnh vực	Học máy	Học máy	Học máy
Họ	Machine learning	Machine learning	Machine learning
Năm ra đời	2018	1996	2006 (book); roots in Kriging, 1951)
Người khởi xướng	Yao, Y.; Vehtari, A.; Simpson, D.; Gelman, A.	Breiman, L.	Rasmussen, C. E. & Williams, C. K. I.
Loại	Bayesian ensemble combination	Ensemble meta-algorithm (variance reduction via bootstrap aggregation)	Probabilistic non-parametric model
Công trình gốc	Yao, Y., Vehtari, A., Simpson, D., & Gelman, A. (2018). Using stacking to average Bayesian predictive distributions. Bayesian Analysis, 13(3), 917–1007.	Breiman, L. (1996). Bagging Predictors. Machine Learning, 24(2), 123–140.	Rasmussen, C. E., & Williams, C. K. I. (2006). Gaussian Processes for Machine Learning. MIT Press. ISBN: 978-0-262-18253-9
Tên gọi khác	Bayesian stacking, Bayesian model stacking, stacking with Bayesian weights, predictive distribution stacking	Bootstrap Aggregating, bootstrap aggregation, bagged ensemble, bagged predictor	GP, Gaussian Process Regression, GPR, Kriging
Liên quan	6	5	3
Tóm tắt	Bayesian stacking combines the predictive distributions of several base models by finding non-negative weights that maximise the leave-one-out log predictive score of the mixture. Formalised by Yao, Vehtari, Simpson, and Gelman (2018), it yields a single calibrated predictive distribution that is provably at least as good as any single constituent model under cross-validation.	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.	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.