A density consistency approach to the inverse Ising problem. Issue 3 (30th March 2021)

• Record Type: Journal Article
• Authors: Braunstein, Alfredo; Catania, Giovanni; Dall'Asta, Luca; Muntoni, Anna Paola
• Is Part Of: Journal of statistical mechanics. Issue 3(2021)
• Publication Date: 2021-03-30
• Subjects: inference of graphical models -- statistical inference -- cavity and replica method; Statistical mechanics -- Periodicals; Mechanics -- Statistical methods -- Periodicals; 530.1305
• DOI: 10.1088/1742-5468/abed43
• Languages: English
• ISSNs: 1742-5468

Abstract

Abstract: We propose a novel approach to the inverse Ising problem which employs the recently introduced density consistency approximation (DC) to determine the model parameters (couplings and external fields) maximizing the likelihood of given empirical data. This method allows for closed-form expressions of the inferred parameters as a function of the first and second empirical moments. Such expressions have a similar structure to the small-correlation expansion derived in reference Sessak and Monasson (2009 J. Phys. A: Math. Theor. 42 055001), of which they provide an improvement in the case of non-zero magnetization at low temperatures, as well as in presence of random external fields. The present work provides an extensive comparison with most common inference methods used to reconstruct the model parameters in several regimes, i.e. by varying both the network topology and the distribution of fields and couplings. The comparison shows that no method is uniformly better than every other one, but DC appears nevertheless as one of the most accurate and reliable approaches to infer couplings and fields from first and second moments in a significant range of parameters.