Connectivity inference with asynchronously updated kinetic Ising models

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dc.contributor Aalto-yliopisto fi
dc.contributor Aalto University en
dc.contributor.advisor Aurell, Erik, Prof., Aalto University, Department of Information and Computer Science, Finland
dc.contributor.author Zeng, Hong-Li
dc.date.accessioned 2014-08-14T09:00:19Z
dc.date.available 2014-08-14T09:00:19Z
dc.date.issued 2014
dc.identifier.isbn 978-952-60-5803-0 (electronic)
dc.identifier.isbn 978-952-60-5802-3 (printed)
dc.identifier.issn 1799-4942 (electronic)
dc.identifier.issn 1799-4934 (printed)
dc.identifier.issn 1799-4934 (ISSN-L)
dc.identifier.uri https://aaltodoc.aalto.fi/handle/123456789/13766
dc.description.abstract This thesis focuses on the inference of network connections from statistical physics point of view. The reconstruction methods of the asynchronously updated kinetic Ising model with an asymmetric Sherrington-Kirkpatrick (SK) model is studied theoretically. Both approximate and exact learning rules for the couplings from the generated dynamical data are developed. The approximate formulae are based on naive mean field (nMF) and Thouless-Anderson-Palmer (TAP) equations respectively. The exact learning rules are derived for two cases: one in which both the spin history and the update times are known and one in which only the spin history. One can average over all possible choices of update times to obtain an averaged learning rule that depends only on spin correlations. We studied all the learning rules numerically. Good convergence is observed in accordance with the theoretical expectations. The developed inference learning rules are applied to two data sets. One is spike trains recorded from 20 retinal ganglion cells and the other is generated by transactions of 100 highly traded stocks on the New York Stock Exchange (NYSE).  For the neuron data set, we compared the inferred asynchronous couplings with the equilibrium ones. The results show that the inferred couplings from these two models are very similar. This implies that real dynamical process of the neuron system satisfies the Gibbs equilibrium conditions and that the final distribution of states is the Gibbs stationary distribution.   For the financial data set, three inference methods are applied to reconstruct the coupling matrices between traded stocks. They are equilibrium, synchronous and asynchronous inference formula respectively. All of them are based on mean-field approximation. Synchronous and asynchronous Ising inference methods give results which are coherent with equilibrium case, but more detailed since the obtained interaction networks are directed. en
dc.format.extent 74 + app. 50
dc.format.mimetype application/pdf en
dc.language.iso en en
dc.publisher Aalto University en
dc.publisher Aalto-yliopisto fi
dc.relation.ispartofseries Aalto University publication series DOCTORAL DISSERTATIONS en
dc.relation.ispartofseries 117/2014
dc.relation.haspart [Publication 1]: Hong-Li Zeng, Erik Aurell, Mikko Alava, and Hamed Mahmoudi. Network inference using asynchronously updated kinetic Ising model. Physical Review E 83, 041135 (2011). DOI: 10.1103/PhysRevE.83.041135
dc.relation.haspart [Publication 2]: Hong-Li Zeng, Mikko Alava, Erik Aurell, John Hertz, and Yasser Roudi. Maximum Likelihood Reconstruction for Ising Models with Asynchronous Updates. Physical Review Letters 110, 210601 (2013). DOI: 10.1103/PhysRevLett.110.210601
dc.relation.haspart [Publication 3]: Hong-Li Zeng, John Hertz, and Yasser Roudi. L1 Regularization for Reconstruction of a Non-equilibrium Ising Model. Accepted by Physica Scripta. arXiv: 1211.3671
dc.relation.haspart [Publication 4]: Hong-Li Zeng, Rémi Lemoy, and Mikko Alava. Financial interaction networks inferred from traded volumes. Journal of Statistical Mechanics: Theory and Experiment P07008 (2014). DOI: 10.1088/1742-5468/2014/07/P07008
dc.subject.other Physics en
dc.title Connectivity inference with asynchronously updated kinetic Ising models en
dc.type G5 Artikkeliväitöskirja fi
dc.contributor.school Perustieteiden korkeakoulu fi
dc.contributor.school School of Science en
dc.contributor.department Teknillisen fysiikan laitos fi
dc.contributor.department Department of Applied Physics en
dc.subject.keyword network inference en
dc.subject.keyword asynchronous update en
dc.subject.keyword kinetic Ising model en
dc.identifier.urn URN:ISBN:978-952-60-5803-0
dc.type.dcmitype text en
dc.type.ontasot Doctoral dissertation (article-based) en
dc.type.ontasot Väitöskirja (artikkeli) fi
dc.contributor.supervisor Alava, Mikko, Prof., Aalto University, Department of Applied Physics, Finland
dc.opn Kühn, Reimer, Prof., King's College London, UK
dc.date.dateaccepted 2014-06-27
dc.contributor.lab Complex Systems and Materials en
dc.rev Professor Manfred Opper
dc.rev Professor Federico Ricci-Tersenghi
dc.date.defence 2014-08-15


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