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Learning and inference in a nonequilibrium Ising model with hidden nodes
Stockholm University, Nordic Institute for Theoretical Physics (Nordita).
2013 (English)In: Physical Review E. Statistical, Nonlinear, and Soft Matter Physics, ISSN 1539-3755, E-ISSN 1550-2376, Vol. 87, no 2, 022127- p.Article in journal (Refereed) Published
Abstract [en]

We study inference and reconstruction of couplings in a partially observed kinetic Ising model. With hidden spins, calculating the likelihood of a sequence of observed spin configurations requires performing a trace over the configurations of the hidden ones. This, as we show, can be represented as a path integral. Using this representation, we demonstrate that systematic approximate inference and learning rules can be derived using dynamical mean-field theory. Although naive mean-field theory leads to an unstable learning rule, taking into account Gaussian corrections allows learning the couplings involving hidden nodes. It also improves learning of the couplings between the observed nodes compared to when hidden nodes are ignored. DOI: 10.1103/PhysRevE.87.022127

Place, publisher, year, edition, pages
2013. Vol. 87, no 2, 022127- p.
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Physical Sciences
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URN: urn:nbn:se:su:diva-88694DOI: 10.1103/PhysRevE.87.022127ISI: 000315151000001OAI: oai:DiVA.org:su-88694DiVA: diva2:612904
Note

AuthorCount:2;

Available from: 2013-03-25 Created: 2013-03-25 Last updated: 2017-12-06Bibliographically approved

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Roudi, Yasser
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