A Bayesian Bi-Directional Splitting Framework for Variable Selection in Large Datasets
大规模数据变量选择的贝叶斯双向拆分框架
Modern tabular datasets are becoming increasingly large, both in the number of samples and covariates, posing significant challenges for Bayesian variable selection due to the resulting computational burden. While there is extensive literature on scaling Bayesian inference to large numbers of observations or high-dimensional covariate spaces, comparatively little work addresses scalable Bayesian variable selection when both dimensions are large simultaneously in a practical…