Bayesian methods
贝叶斯岭回归
贝叶斯岭回归是岭回归的一种概率形式，由 David J. C. MacKay 于 1992 年提出。在该方法中，正则化强度和噪声精度不是由分析者固定，而是通过最大化观测数据的边际似然（证据）来自动估计。其结果是得到回归权重上的完整后验分布，以及校准过的预测不确定性。
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来源
- MacKay, D. J. C. (1992). Bayesian Interpolation. Neural Computation, 4(3), 415–447. DOI: 10.1162/neco.1992.4.3.415 ↗
- Bishop, C. M. (2006). Pattern Recognition and Machine Learning (Ch. 3). Springer. ISBN: 978-0-387-31073-2
如何引用本页
ScholarGate. (2026, June 3). Bayesian Ridge Regression (MacKay Probabilistic Regularisation). ScholarGate. https://scholargate.app/zh/machine-learning/bayesian-ridge-regression
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