Bayesian methods
贝叶斯模型平均 (Bayesian Model Averaging, BMA)
由 Hoeting, Madigan, Raftery 和 Volinsky 于 1999 年作为教程正式提出的贝叶斯模型平均 (Bayesian Model Averaging, BMA) 通过对所有合理的模型设定进行平均来处理模型不确定性，而不是选择单一的最佳模型。每个候选模型都会获得一个后验概率，该概率反映了在给定先验的情况下模型拟合数据的程度，并且预测或系数估计是通过对整个模型空间进行加权平均而形成的。这种方法减少了当将单一选定的模型视为真实模型时产生的偏差和过度自信。
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Method map
The neighbourhood of related methods — select a node to explore.
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来源
- Hoeting, J. A., Madigan, D., Raftery, A. E. & Volinsky, C. T. (1999). Bayesian Model Averaging: A Tutorial. Statistical Science, 14(4), 382–401. link ↗
- Zeugner, S. & Feldkircher, M. (2015). Bayesian Model Averaging Employing Fixed and Flexible Priors: The BMS Package for R. Journal of Statistical Software, 68(4), 1–37. DOI: 10.18637/jss.v068.i04 ↗
如何引用本页
ScholarGate. (2026, June 1). Bayesian Model Averaging. ScholarGate. https://scholargate.app/zh/bayesian/bayesian-model-averaging
Which method?
Set this method beside its closest kin and read them side by side — the library lays the books on the table; the choice is yours.
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