Momentum-Space Renormalization Group Transformation in Bayesian Image Modeling by Gaussian Graphical Model

摘要

A new Bayesian modeling method is proposed by combining the maximization of the marginal likelihood with a momentum-space renormalization group transformation for Gaussian graphical models. Moreover, we present a scheme for computing the statistical averages of hyperparameters and mean square errors in our proposed method based on a momentum-space renormalization group transformation.