Momentum-Space Renormalization Group Transformation in Bayesian Image Modeling by Gaussian Graphical Model
This work addresses image modeling for Bayesian inference, but appears incremental as it builds on existing Gaussian graphical model techniques.
The paper tackles Bayesian image modeling by introducing a new method that combines marginal likelihood maximization with momentum-space renormalization group transformations for Gaussian graphical models, resulting in a scheme for computing hyperparameter averages and mean square errors.
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.