Bayesian methods
贝叶斯逻辑回归
贝叶斯逻辑回归是一种分类模型，它将贝叶斯推断应用于二元或多项结果的逻辑（sigmoid）似然函数。该模型在 Gelman、Jakulin、Pittau 和 Su (2008) 正式化的弱信息先验框架内发展，为系数设置了先验分布，并将该先验与数据似然函数相结合，从而为每个参数生成完整的后验分布——即使在小样本、稀有事件设置或频率学派最大似然估计失效的完全分离情况下，也能提供校准的类别概率和真实的置信度。
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来源
- Gelman, A., Jakulin, A., Pittau, M. G. & Su, Y.-S. (2008). A Weakly Informative Default Prior Distribution for Logistic and Other Regression Models. Annals of Applied Statistics, 2(4), 1360–1383. DOI: 10.1214/08-AOAS191 ↗
如何引用本页
ScholarGate. (2026, June 1). Bayesian Logistic Regression. ScholarGate. https://scholargate.app/zh/bayesian/bayesian-logistic-regression
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