English(EN) A Flexible Empirical Bayes Approach to Generalized Linear Models, with Applications to Sparse Logistic Regression

新的经验贝叶斯方法增强了广义线性模型

作者 PulseAugur 编辑部 · [1 个来源] · 2026-08-28 04:00

研究人员开发了一种新颖的经验贝叶斯方法来拟合贝叶斯广义线性模型，引入了一种均场变分推断方法，该方法在算法内估计先验，使其无需调优。该方法优化后验均值和先验参数，支持 L-BFGS 和随机梯度下降等可扩展优化算法。该框架统一了指数族分布，并在稀疏逻辑回归应用中展示了优于现有方法的预测性能。 AI

影响引入了一种更灵活、更有效率的统计建模方法，有可能提高稀疏逻辑回归等机器学习应用的性能。

排序理由该集群包含一篇在 arXiv 上发表的关于新统计方法的论文。[lever_c_demoted from research: ic=1 ai=1.0]

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本文如何被排名

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arXiv stat.ML TIER_1 English(EN) · Dongyue Xie, Matthew Stephens · 2026-08-28 04:00

广义线性模型的一种灵活经验贝叶斯方法及其在稀疏逻辑回归中的应用

arXiv:2601.21217v2 Announce Type: replace Abstract: We introduce a flexible empirical Bayes approach for fitting Bayesian generalized linear models. Specifically, we adopt a novel mean-field variational inference (VI) method and the prior is estimated within the VI algorithm, mak…