Spectral Collapsed Gibbs Sampler for Bayesian Sparse Regression
贝叶斯稀疏回归的谱塌缩 Gibbs 采样器
Sparse regression based on global-local shrinkage priors are increasingly used for Bayesian modeling of modern high-dimensional data, but scaling up the Gibbs sampler for posterior inference remains a challenge. While much effort has gone into speeding up the high-dimensional coefficient update step, insufficient attention has been given to the potential poor mixing of the global scale parameter $τ$ and of the overall sampler. One proposed remedy has been to marginalize out…