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Related Experiment Videos

Learning and inference in a nonequilibrium Ising model with hidden nodes.

Benjamin Dunn1, Yasser Roudi

  • 1The Kavli Institue for Systems Neuroscience, NTNU, 7030 Trondheim. benjamin.dunn@ntnu.no

Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics
|March 19, 2013
PubMed
Summary
This summary is machine-generated.

We developed a path integral method to infer couplings in partially observed kinetic Ising models. This approach, using dynamical mean-field theory with Gaussian corrections, successfully learns couplings involving hidden spins.

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Area of Science:

  • Statistical physics
  • Computational neuroscience
  • Machine learning

Background:

  • Kinetic Ising models are used to model complex systems with interacting components.
  • Partial observation, where some components are hidden, complicates model inference.
  • Accurate reconstruction of model parameters (couplings) is crucial for understanding system dynamics.

Purpose of the Study:

  • To develop a method for inferring couplings in partially observed kinetic Ising models.
  • To utilize path integral and dynamical mean-field theory for approximate inference.
  • To improve the learning of couplings involving hidden components.

Main Methods:

  • Representing the likelihood calculation as a path integral over hidden spin configurations.
  • Deriving approximate inference and learning rules using dynamical mean-field theory.
  • Incorporating Gaussian corrections to stabilize and enhance the learning process.

Main Results:

  • The path integral representation enables systematic approximate inference.
  • Naive mean-field theory yields an unstable learning rule.
  • Dynamical mean-field theory with Gaussian corrections allows learning couplings involving hidden nodes.
  • This method improves the learning of couplings between observed nodes compared to ignoring hidden nodes.

Conclusions:

  • The proposed path integral approach with dynamical mean-field theory and Gaussian corrections is effective for inferring couplings in partially observed kinetic Ising models.
  • This method offers a significant improvement over traditional approaches, especially when dealing with hidden components.
  • The findings have implications for understanding and modeling complex systems with unobserved variables.