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

  title={Momentum-Space Renormalization Group Transformation in Bayesian Image Modeling by Gaussian Graphical Model},
  author={Kazuyuki Tanaka and Masamichi Nakamura and Shun'ichi Kataoka and Masayuki Ohzeki and Muneki Yasuda},
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. 
1 Citations
Sublinear Computational Time Modeling by Momentum-Space Renormalization Group Theory in Statistical Machine Learning Procedures
The modeling scheme has been proposed and the basic frameworks have been briefly explained, and some numerical experimental results of sublinear computational time modeling based on the momentum-space renormalization scheme are presented. Expand


Field Theory, the Renormalization Group, and Critical Phenomena: Graphs to Computers
Pertinent Concepts and Ideas in the Theory of Critical Phenomena Formulation of the Problem of Phase Transitions in Terms of Functional Integrals Functional Integrals in Quantum Field TheoryExpand
Probabilistic Graphical Models - Principles and Techniques
The framework of probabilistic graphical models, presented in this book, provides a general approach for causal reasoning and decision making under uncertainty, allowing interpretable models to be constructed and then manipulated by reasoning algorithms. Expand
Information, Physics and Computation (Oxford
  • 2009