Journal
JOURNAL OF STATISTICAL MECHANICS-THEORY AND EXPERIMENT
Volume -, Issue -, Pages -Publisher
IOP PUBLISHING LTD
DOI: 10.1088/1742-5468/2012/03/P03004
Keywords
data mining (theory); network reconstruction; learning theory; statistical inference
Categories
Funding
- Deutsche Forschungsgemeinschaft (DFG) [SFB 680]
Ask authors/readers for more resources
We apply the Bethe-Peierls approximation to the inverse Ising problem and show how the linear response relation leads to a simple method for reconstructing couplings and fields of the Ising model. This reconstruction is exact on tree graphs, yet its computational expense is comparable to those of other mean-field methods. We compare the performance of this method to the independent-pair, naive mean-field, and Thouless-Anderson-Palmer approximations, the Sessak-Monasson expansion, and susceptibility propagation on the Cayley tree, SK model and random graph with fixed connectivity. At low temperatures, Bethe reconstruction outperforms all of these methods, while at high temperatures it is comparable to the best method available so far ( the Sessak-Monasson method). The relationship between Bethe reconstruction and other mean-field methods is discussed.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available