Approximating posteriors with high-dimensional nuisance parameters via integrated rotated Gaussian approximation
通过积分旋转高斯近似处理高维干扰参数的后验近似
Summary Posterior computation for high-dimensional data with many parameters can be challenging. This article focuses on a new method for approximating posterior distributions of a low- to moderate-dimensional parameter in the presence of a high-dimensional or otherwise computationally challenging nuisance parameter. The focus is on regression models and the key idea is to separate the likelihood into two components through a rotation. One component involves only the…