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

03/20/2018
by   Kazuyuki Tanaka, et al.
0

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.

READ FULL TEXT
research
01/05/2015

Inverse Renormalization Group Transformation in Bayesian Image Segmentations

A new Bayesian image segmentation algorithm is proposed by combining a l...
research
12/07/2018

Rank Likelihood for Bayesian Nonparanormal Graphical Models

Gaussian graphical models, where it is assumed that the variables of int...
research
06/30/2023

High-Dimensional Bayesian Structure Learning in Gaussian Graphical Models using Marginal Pseudo-Likelihood

Gaussian graphical models depict the conditional dependencies between va...
research
09/30/2019

A random covariance model for bi-level graphical modeling with application to resting-state fMRI data

This paper considers a novel problem, bi-level graphical modeling, in wh...
research
06/15/2018

Robust Bayesian Model Selection for Variable Clustering with the Gaussian Graphical Model

Variable clustering is important for explanatory analysis. However, only...
research
05/23/2021

A Bayesian approach for partial Gaussian graphical models with sparsity

We explore various Bayesian approaches to estimate partial Gaussian grap...
research
03/03/2023

Deep Momentum Multi-Marginal Schrödinger Bridge

Reconstructing population dynamics using only samples from distributions...

Please sign up or login with your details

Forgot password? Click here to reset