方法对比

	贝叶斯堆叠集成	Bagging（Bootstrap Aggregating）	贝叶斯模型平均 (Bayesian Model Averaging, BMA)
领域	机器学习	机器学习	贝叶斯
方法族	Machine learning	Machine learning	Bayesian methods
起源年份	2018	1996	1999
提出者	Yao, Y.; Vehtari, A.; Simpson, D.; Gelman, A.	Breiman, L.	Hoeting, Madigan, Raftery & Volinsky
类型	Bayesian ensemble combination	Ensemble meta-algorithm (variance reduction via bootstrap aggregation)	Bayesian model averaging
开创性文献	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.	Hoeting, J. A., Madigan, D., Raftery, A. E. & Volinsky, C. T. (1999). Bayesian Model Averaging: A Tutorial. Statistical Science, 14(4), 382–401.
别名	Bayesian stacking, Bayesian model stacking, stacking with Bayesian weights, predictive distribution stacking	Bootstrap Aggregating, bootstrap aggregation, bagged ensemble, bagged predictor	BMA, Bayesian model combination, Bayesian Model Ortalaması (BMA)
相关	6	5	5
摘要	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.	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.