Advances In Physics· 2017Q1· Review
Inverse statistical problems: from the inverse Ising problem to data science
- 263citations
- Q1SCImago
- 2017year
Short summary
This review details how inverse statistical physics problems, particularly the inverse Ising problem, are crucial for inferring model parameters from observed data, with applications ranging from biology to neural networks.
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Key points
- Inverse statistical physics problems infer model parameters from observations, unlike traditional methods.
- The inverse Ising problem specifically reconstructs spin coupling strengths from spin correlation data.
- Pseudolikelihood is a strong statistical method for solving the inverse Ising problem.
- Applications include neural connection reconstruction, protein structure determination, and gene regulatory network inference.
AI-generated from the title and abstract; the full text is not read.
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.
The authors' abstract, as published at the source. Advances In Physics, 2017 · DOI ↗
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Field: Mathematical Physics
Mathematical PhysicsMathematics