A majorization–minimization approach to variable selection using spike and slab priors
基于尖峰与厚尾先验的变量选择：上界化—最小化方法
We develop a method to carry out MAP estimation for a class of Bayesian regression models in which coefficients are assigned with Gaussian-based spike and slab priors. The objective function in the corresponding optimization problem has a Lagrangian form in that regression coefficients are regularized by a mixture of squared l2 and l0 norms. A tight approximation to the l0 norm using majorization–minimization techniques is derived, and a coordinate descent algorithm in…