Machine learningMachine learning
贝叶斯装袋法
贝叶斯装袋法用贝叶斯装袋法替代了经典装袋法——它为训练样本绘制狄利克雷分布的权重，而不是有放回地抽样——并在这些权重下训练一组基学习器。其结果是一个原则性的集成模型，它近似了贝叶斯后验预测分布，从而在提供高预测精度的同时，也给出了校准过的置信度估计。
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来源
- Clyde, M. & Lee, H. (2001). Bagging and the Bayesian bootstrap. In T. Richardson & T. Jaakkola (Eds.), Proceedings of the Eighth International Workshop on Artificial Intelligence and Statistics (AISTATS 2001). link ↗
- Rubin, D. B. (1981). The Bayesian bootstrap. The Annals of Statistics, 9(1), 130–134. DOI: 10.1214/aos/1176345338 ↗
如何引用本页
ScholarGate. (2026, June 3). Bayesian Bagging (Bootstrap Aggregation with Bayesian Bootstrap). ScholarGate. https://scholargate.app/zh/machine-learning/bayesian-bagging
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