Susceptibility Propagation by Using Diagonal Consistency
This work provides an incremental improvement for researchers in statistical physics and machine learning working on inverse Ising problems.
The authors tackled the problem of approximate computation for Markov random fields by formulating an improved susceptibility propagation method using a diagonal matching concept, which is robust across various network structures and reduces to existing methods in special cases.
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.