Adaptive Bayesian Estimation via Block Prior
نویسندگان
چکیده
A novel block prior is proposed for adaptive Bayesian estimation. The prior does not depend on the smoothness of the function and the sample size. It puts sufficient prior mass near the true signal and automatically concentrates on its effective dimension. A rateoptimal posterior contraction is obtained in a general framework, which includes density estimation, white noise model, Gaussian sequence model, Gaussian regression and spectral density estimation.
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