Bethe-Peierls approximation and the inverse Ising problem

被引:38
|
作者
Nguyen, H. Chau [1 ]
Berg, Johannes [1 ]
机构
[1] Univ Cologne, Inst Theoret Phys, D-50937 Cologne, Germany
关键词
data mining (theory); network reconstruction; learning theory; statistical inference; STATISTICAL PHYSICS; SOLVABLE MODEL; INFERENCE;
D O I
10.1088/1742-5468/2012/03/P03004
中图分类号
O3 [力学];
学科分类号
08 ; 0801 ;
摘要
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.
引用
收藏
页数:9
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