Rate exact Bayesian adaptation with modified block priors
提出一种不依赖函数光滑性或样本大小的块先验，用于自适应贝叶斯估计，在密度估计、白噪声模型等框架下实现速率最优的后验收缩。
A novel block prior is proposed for adaptive Bayesian estimation. The prior does not depend on the smoothness of the function or the sample size. It puts sufficient prior mass near the true signal and automatically concentrates on its effective dimension. A rate-optimal posterior contraction is obtained in a general framework, which includes density estimation, white noise model, Gaussian sequence model, Gaussian regression and spectral density estimation.