6 citations · 14 across the 5 of their papers we have counts for
6 papers
Momentum-Space Renormalization Group Transformation in Bayesian Image Modeling by Gaussian Graphical Model
Kazuyuki Tanaka, Masamichi Nakamura, Shun Kataoka +2
A new Bayesian modeling method is proposed by combining the maximization of the marginal likelihood with a momentum-space renormalization group transformation for Gaussian graphica…
Linear-Time Algorithm in Bayesian Image Denoising based on Gaussian Markov Random Field
Muneki Yasuda, Junpei Watanabe, Shun Kataoka +1
In this paper, we consider Bayesian image denoising based on a Gaussian Markov random field (GMRF) model, for which we propose an new algorithm. Our method can solve Bayesian image…
Solving Non-parametric Inverse Problem in Continuous Markov Random Field using Loopy Belief Propagation
Muneki Yasuda, Shun Kataoka
In this paper, we address the inverse problem, or the statistical machine learning problem, in Markov random fields with a non-parametric pair-wise energy function with continuous…
Community Detection Algorithm Combining Stochastic Block Model and Attribute Data Clustering
Shun Kataoka, Takuto Kobayashi, Muneki Yasuda +1
We propose a new algorithm to detect the community structure in a network that utilizes both the network structure and vertex attribute data. Suppose we have the network structure…
Statistical Analysis of Loopy Belief Propagation in Random Fields
Muneki Yasuda, Shun Kataoka, Kazuyuki Tanaka
Loopy belief propagation (LBP), which is equivalent to the Bethe approximation in statistical mechanics, is a message-passing-type inference method that is widely used to analyze s…
Inverse Renormalization Group Transformation in Bayesian Image Segmentations
Kazuyuki Tanaka, Shun Kataoka, Muneki Yasuda +1
A new Bayesian image segmentation algorithm is proposed by combining a loopy belief propagation with an inverse real space renormalization group transformation to reduce the comput…