Susceptibility Propagation by Using Diagonal Consistency
Statistical Mechanics
2017-12-04 v1 Statistics Theory
Machine Learning
Statistics Theory
Abstract
A susceptibility propagation that is constructed by combining a belief propagation and a linear response method is used for approximate computation for Markov random fields. Herein, we formulate a new, improved susceptibility propagation by using the concept of a diagonal matching method that is based on mean-field approaches to inverse Ising problems. The proposed susceptibility propagation is robust for various network structures, and it is reduced to the ordinary susceptibility propagation and to the adaptive Thouless-Anderson-Palmer equation in special cases.
Cite
@article{arxiv.1712.00155,
title = {Susceptibility Propagation by Using Diagonal Consistency},
author = {Muneki Yasuda and Kazuyuki Tanaka},
journal= {arXiv preprint arXiv:1712.00155},
year = {2017}
}