方法对比

	贝叶斯模型平均 (Bayesian Model Averaging, BMA)	Boosting	高斯过程
领域	贝叶斯	机器学习	机器学习
方法族	Bayesian methods	Machine learning	Machine learning
起源年份	1999	1990–1997	2006 (book); roots in Kriging, 1951)
提出者	Hoeting, Madigan, Raftery & Volinsky	Schapire, R. E.; Freund, Y.	Rasmussen, C. E. & Williams, C. K. I.
类型	Bayesian model averaging	Sequential ensemble (iterative reweighting)	Probabilistic non-parametric model
开创性文献	Hoeting, J. A., Madigan, D., Raftery, A. E. & Volinsky, C. T. (1999). Bayesian Model Averaging: A Tutorial. Statistical Science, 14(4), 382–401.	Freund, Y. & Schapire, R. E. (1997). A decision-theoretic generalization of on-line learning and an application to boosting. Journal of Computer and System Sciences, 55(1), 119–139.	Rasmussen, C. E., & Williams, C. K. I. (2006). Gaussian Processes for Machine Learning. MIT Press. ISBN: 978-0-262-18253-9
别名	BMA, Bayesian model combination, Bayesian Model Ortalaması (BMA)	AdaBoost, gradient boosting, iterative reweighting ensemble, sequential ensemble	GP, Gaussian Process Regression, GPR, Kriging
相关	5	6	3
摘要	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.	Boosting is a sequential ensemble technique that converts many simple, barely-better-than-chance learners into a single highly accurate model by repeatedly focusing training on the examples that previous learners got wrong, then combining all learners with weights proportional to their individual accuracy.	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.