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Maximum Likelihood Reconstruction for Ising Models with Asynchronous Updates
KTH, School of Computer Science and Communication (CSC), Computational Biology, CB. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
KTH, Centres, Nordic Institute for Theoretical Physics NORDITA.
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2013 (English)In: Physical Review Letters, ISSN 0031-9007, E-ISSN 1079-7114, Vol. 110, no 21, 210601- p.Article in journal (Refereed) Published
Abstract [en]

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.

Place, publisher, year, edition, pages
2013. Vol. 110, no 21, 210601- p.
National Category
Physical Sciences
Identifiers
URN: urn:nbn:se:kth:diva-124289DOI: 10.1103/PhysRevLett.110.210601ISI: 000319257200001Scopus ID: 2-s2.0-84877933155OAI: oai:DiVA.org:kth-124289DiVA: diva2:634726
Note

QC 20130701

Available from: 2013-07-01 Created: 2013-06-28 Last updated: 2017-12-06Bibliographically approved

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