A variational Bayes approach to inference for low-dimensional parameters in high-dimensional linear regression
高维线性回归中低维目标参数推断的变分贝叶斯方法
We propose a scalable variational Bayes method for statistical inference for a single or pre-specified low-dimensional subset of the coordinates of a high-dimensional parameter in sparse linear regression. Our approach relies on assigning a mean-field approximation to the nuisance coordinates and carefully modelling the conditional distribution of the target given the nuisance. This requires only a preprocessing step and preserves the computational advantages of mean-field…