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Maximum likelihood reconstruction for Ising models with asynchronous updates.

Hong-Li Zeng1, Mikko Alava, Erik Aurell

  • 1Department of Applied Physics, Aalto University, FIN-00076 Aalto, Finland. hong.zeng@aalto.fi

Physical Review Letters
|June 11, 2013
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Summary
This summary is machine-generated.

Researchers developed a method to infer couplings in asynchronous kinetic Ising models. This technique, applicable with or without update time data, relies on spin correlations and shows good convergence in simulations.

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Area of Science:

  • Statistical physics
  • Computational neuroscience
  • Machine learning

Background:

  • The kinetic Ising model is a fundamental tool for studying systems with discrete states and interactions.
  • Inferring model parameters (couplings) is crucial for understanding complex systems but challenging in asynchronous settings.

Purpose of the Study:

  • To develop and validate methods for inferring couplings in asynchronous kinetic Ising models.
  • To explore the impact of available information (spin history and update times) on inference accuracy.

Main Methods:

  • Derivation of a learning rule based on spin correlations by averaging over update times.
  • Application of a decoupling approximation for inference using only spin history.
  • Numerical simulations on fully asymmetric Sherrington-Kirkpatrick models.

Main Results:

  • A robust learning rule was derived, dependent solely on spin correlations.
  • The rule was shown to be effective even when update times are unknown, using a decoupling approximation.
  • Numerical studies demonstrated good convergence across various system sizes, temperatures, and external fields.

Conclusions:

  • The proposed methods provide an effective way to infer couplings in asynchronous kinetic Ising models.
  • The findings are applicable to diverse fields requiring the analysis of dynamic systems with interacting components.