Inverse statistical problems: from the inverse Ising problem to data science. Issue 3 (3rd July 2017)

• Record Type: Journal Article
• Authors: Nguyen, H. Chau; Zecchina, Riccardo; Berg, Johannes
• Is Part Of: Advances in physics. Volume 66:Issue 3(2017)
• Page Start: 197
• Page End: 261
• Publication Date: 2017-07-03
• Subjects: 02.30.Zz inverse problems -- 02.50.Tt inference methods -- 89.75.-k complex systems; inverse problems -- inference methods -- network reconstruction -- data analysis -- statistical physics of complex systems; Physics -- Periodicals; 530
• DOI: 10.1080/00018732.2017.1341604
• Languages: English
• ISSNs: 0001-8732

Abstract

Abstract : Inverse problems in statistical physics are motivated by the challenges of 'big data' in different fields, in particular high-throughput experiments in biology. In inverse problems, the usual procedure of statistical physics needs to be reversed: Instead of calculating observables on the basis of model parameters, we seek to infer parameters of a model based on observations. In this review, we focus on the inverse Ising problem and closely related problems, namely how to infer the coupling strengths between spins given observed spin correlations, magnetizations, or other data. We review applications of the inverse Ising problem, including the reconstruction of neural connections, protein structure determination, and the inference of gene regulatory networks. For the inverse Ising problem in equilibrium, a number of controlled and uncontrolled approximate solutions have been developed in the statistical mechanics community. A particularly strong method, pseudolikelihood, stems from statistics. We also review the inverse Ising problem in the non-equilibrium case, where the model parameters must be reconstructed based on non-equilibrium statistics.