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Maximum Likelihood Reconstruction for Ising Models with Asynchronous Updates
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© 2013 American Physical Society (APS). This is the accepted version of the following article: Zeng, Hong-Li & Alava, Mikko J. & Aurell, Erik & Hertz, John & Roudi, Yasser. 2013. Maximum Likelihood Reconstruction for Ising Models with Asynchronous Updates. Physical Review Letters. Volume 110, Issue 21. 210601/1-5. ISSN 0031-9007 (printed). DOI: 10.1103/physrevlett.110.210601, which has been published in final form at http://journals.aps.org/prl/abstract/10.1103/PhysRevLett.110.210601.
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A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
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en
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210601/1-5
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Physical Review Letters, Volume 110, Issue 21
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We describe how the couplings in an asynchronous kinetic Ising model can be inferred. We consider two cases: one in which we know both the spin history and the update times and one in which we know only the spin history. For the first case, we show that one can average over all possible choices of update times to obtain a learning rule that depends only on spin correlations and can also be derived from the equations of motion for the correlations. For the second case, the same rule can be derived within a further decoupling approximation. We study all methods numerically for fully asymmetric Sherrington-Kirkpatrick models, varying the data length, system size, temperature, and external field. Good convergence is observed in accordance with the theoretical expectations.
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Zeng, Hong-Li & Alava, Mikko J. & Aurell, Erik & Hertz, John & Roudi, Yasser. 2013. Maximum Likelihood Reconstruction for Ising Models with Asynchronous Updates. Physical Review Letters. Volume 110, Issue 21. 210601/1-5. ISSN 0031-9007 (printed). DOI: 10.1103/physrevlett.110.210601.