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Posterior contraction for empirical bayesian approach to inverse problems under non-diagonal assumption

  • * Corresponding author: Jigen Peng

    * Corresponding author: Jigen Peng 
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  • We investigate an empirical Bayesian nonparametric approach to a family of linear inverse problems with Gaussian prior and Gaussian noise. We consider a class of Gaussian prior probability measures with covariance operator indexed by a hyperparameter that quantifies regularity. By introducing two auxiliary problems, we construct an empirical Bayes method and prove that this method can automatically select the hyperparameter. In addition, we show that this adaptive Bayes procedure provides optimal contraction rates up to a slowly varying term and an arbitrarily small constant, without knowledge about the regularity index. Our method needs not the prior covariance, noise covariance and forward operator have a common basis in their singular value decomposition, enlarging the application range compared with the existing results. A simple simulation example is given that illustrates the effectiveness of the proposed method.

    Mathematics Subject Classification: Primary: 35R30, 35R25; Secondary: 45Q05.


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  • Figure 1.  Left panels the empirical Bayes posterior mean (red) and the true curve (blue, dashed). Right panels corresponding normalized likelihood for $ \hat{\tilde{\alpha}} $ (regularity index for the artificial diagonal problem). We have $ n = 10^3, 10^5, 10^8, $ and $ 10^{12} $, from top to bottom

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