2012/04/30 by H. Chau Nguyen, Johannes Berg · 2 citations
Biochemistry, Genetics and Molecular Biology · Mathematics · Physics and Astronomy · #Computer science #Condensed matter physics #Data mining #Field (mathematics) #Gene Regulatory Network Analysis #Inverse #Ising model #Ising spin #Mathematics #Mean field theory #Measure (data warehouse) #Observable #Physics #Protein Structure and Dynamics #Quantum mechanics #Spin (aerodynamics) #Spins #Square-lattice Ising model #Statistical physics #Theoretical and Computational Physics #Thermodynamics #cond-mat.dis-nn #cond-mat.stat-mech #q-bio.QM
paper · pdf · doi:10.1103/physrevlett.109.050602
published as Phys. Rev. Lett. 109, 050602 (2012)
openalex publication_date 2012/08/01 · arxiv created 2012/08/10 · arxiv updated 2012/08/13 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
The large amounts of data from molecular biology and neuroscience have lead to a renewed interest in the inverse Ising problem: how to reconstruct parameters of the Ising model (couplings between spins and external fields) from a number of spin configurations sampled from the Boltzmann measure. To invert the relationship between model parameters and observables (magnetizations and correlations), mean-field approximations are often used, allowing the determination of model parameters from data. However, all known mean-field methods fail at low temperatures with the emergence of multiple thermodynamic states. Here, we show how clustering spin configurations can approximate these thermodynamic states and how mean-field methods applied to thermodynamic states allow an efficient reconstruction of Ising models also at low temperatures.