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Found 10 papers, 1 papers with code
Momentum-Space Renormalization Group Transformation in Bayesian Image Modeling by Gaussian Graphical Model
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.
Linear-Time Algorithm in Bayesian Image Denoising based on Gaussian Markov Random Field
In this paper, we consider Bayesian image denoising based on a Gaussian Markov random field (GMRF) model, for which we propose an new algorithm.
Solving Non-parametric Inverse Problem in Continuous Markov Random Field using Loopy Belief Propagation
The exact treatment of maximum likelihood estimation is intractable because of two problems: (1) it includes the evaluation of the partition function and (2) it is formulated in the form of functional optimization.
Community Detection Algorithm Combining Stochastic Block Model and Attribute Data Clustering
We propose a new algorithm to detect the community structure in a network that utilizes both the network structure and vertex attribute data.
Statistical Analysis of Loopy Belief Propagation in Random Fields
In the latter part of this paper, we describe the application of the proposed method to Bayesian image restoration, in which we observed that our theoretical results are in good agreement with the numerical results for natural images.
Inverse Renormalization Group Transformation in Bayesian Image Segmentations
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 computational time.
Composite Likelihood Estimation for Restricted Boltzmann machines
Learning the parameters of graphical models using the maximum likelihood estimation is generally hard which requires an approximation.
Bayesian Reconstruction of Missing Observations
We focus on an interpolation method referred to Bayesian reconstruction in this paper.
Bayesian image segmentations by Potts prior and loopy belief propagation
This paper presents a Bayesian image segmentation model based on Potts prior and loopy belief propagation.
Traffic data reconstruction based on Markov random field modeling
We consider the traffic data reconstruction problem.
