方法对比

	稳健贝叶斯模型平均	贝叶斯模型平均 (Bayesian Model Averaging, BMA)
领域	贝叶斯	贝叶斯
方法族	Bayesian methods	Bayesian methods
起源年份	1999–2012	1999
提出者	Hoeting, Madigan, Raftery, Volinsky (BMA); robustness extensions by Ley & Steel and others	Hoeting, Madigan, Raftery & Volinsky
类型	Bayesian model selection and averaging	Bayesian model averaging
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.	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	robust BMA, outlier-robust BMA, robust model averaging, heavy-tailed BMA	BMA, Bayesian model combination, Bayesian Model Ortalaması (BMA)
相关	6	5
摘要	Robust Bayesian model averaging extends standard BMA by replacing sensitive conjugate priors with heavy-tailed or mixture priors (e.g., mixtures of g-priors), and optionally robust likelihoods, so that posterior model probabilities and averaged estimates remain stable when data contain outliers, influential observations, or when the prior on model parameters would otherwise dominate the results.	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.