Comparar métodos

	Ensemble de Empilhamento Bayesiano	Bagging (Bootstrap Aggregating)	Média Bayesiana de Modelos	Processo Gaussiano
Área	Aprendizado de máquina	Aprendizado de máquina	Bayesiano	Aprendizado de máquina
Família	Machine learning	Machine learning	Bayesian methods	Machine learning
Ano de origem	2018	1996	1999	2006 (book); roots in Kriging, 1951)
Autor original	Yao, Y.; Vehtari, A.; Simpson, D.; Gelman, A.	Breiman, L.	Hoeting, Madigan, Raftery & Volinsky	Rasmussen, C. E. & Williams, C. K. I.
Tipo	Bayesian ensemble combination	Ensemble meta-algorithm (variance reduction via bootstrap aggregation)	Bayesian model averaging	Probabilistic non-parametric model
Fonte seminal	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.	Rasmussen, C. E., & Williams, C. K. I. (2006). Gaussian Processes for Machine Learning. MIT Press. ISBN: 978-0-262-18253-9
Outros nomes	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)	GP, Gaussian Process Regression, GPR, Kriging
Relacionados	6	5	5	3
Resumo	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.	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.