论文标题
贝叶斯多种逻辑回归多种类别
Bayesian Multinomial Logistic Regression for Numerous Categories
论文作者
论文摘要
虽然多项式逻辑回归是在多个类别之间进行分类的有用工具，但是当类别数量较大时，贝叶斯实现的后验采样在计算上是繁重的。在本文中，我们表明，适当的数据增强技术可提供比文献中替代方案更快的后验采样。这种速度来自两个来源：系数上的更简单的后验条件分布以及平行参数绘制的能力。在模拟研究中，我们证明，即使没有平行的计算，我们的后验抽样方法的有效采样率是竞争方法的两倍，即使使用大量类别。此外，此计算时间仅随着类别数量的增加而线性增加。我们相应的R软件包可在GitHub上找到。
While multinomial logistic regression is a useful tool for classification among multiple categories, the posterior sampling of Bayesian implementations is computationally burdensome when the number of categories is large. In this paper, we show that the appropriate data augmentation technique provides faster posterior sampling than alternatives in the literature. This speed up comes from two sources: simpler posterior conditional distributions on the coefficients and the ability to parallelize parameter draws. In simulation studies, we demonstrate that the effective sampling rate of our posterior sampling approach is double that of competing methods when working with a large number of categories, even without parallelized computations. Furthermore, this computation time only increases linearly as the number of categories increases. Our corresponding R package is available on Github.
