So sánh phương pháp

	Tập hợp xếp chồng Bayes	Bayesian Model Averaging
Lĩnh vực	Học máy	Bayes
Họ	Machine learning	Bayesian methods
Năm ra đời	2018	1999
Người khởi xướng	Yao, Y.; Vehtari, A.; Simpson, D.; Gelman, A.	Hoeting, Madigan, Raftery & Volinsky
Loại	Bayesian ensemble combination	Bayesian model averaging
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.
Tên gọi khác	Bayesian stacking, Bayesian model stacking, stacking with Bayesian weights, predictive distribution stacking	BMA, Bayesian model combination, Bayesian Model Ortalaması (BMA)
Liên quan	6	5
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.