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

TL;DR

This paper introduces a novel Bayesian image modeling approach that integrates momentum-space renormalization group transformations with Gaussian graphical models to improve hyperparameter estimation and error analysis.

Contribution

It combines marginal likelihood maximization with renormalization group techniques for Gaussian graphical models, offering a new framework for Bayesian image analysis.

Findings

Effective computation of hyperparameter averages

Improved mean square error estimation

Enhanced Bayesian modeling capabilities

Abstract

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 computint the statistical averages of hyperparameters and mean square errors in our proposed method based on a momentumspace renormalization transformation.