So sánh phương pháp

	Tập hợp xếp chồng Bayes	Bayesian Model Averaging	Quá trình Gauss
Lĩnh vực	Học máy	Bayes	Học máy
Họ	Machine learning	Bayesian methods	Machine learning
Năm ra đời	2018	1999	2006 (book); roots in Kriging, 1951)
Người khởi xướng	Yao, Y.; Vehtari, A.; Simpson, D.; Gelman, A.	Hoeting, Madigan, Raftery & Volinsky	Rasmussen, C. E. & Williams, C. K. I.
Loại	Bayesian ensemble combination	Bayesian model averaging	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.	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
Tên gọi khác	Bayesian stacking, Bayesian model stacking, stacking with Bayesian weights, predictive distribution stacking	BMA, Bayesian model combination, Bayesian Model Ortalaması (BMA)	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.	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.