There are many methods proposed for inferring parameters of the Ising modelfrom given data, that is a set of configurations generated according to themodel itself. However little attention has been paid until now to the data, e.g.how the data is generated, whether the inference error using one set of datacould be smaller than using another set of data, etc. In this paper we discussthe data quality problem in the inverse Ising problem, using as a benchmarkthe kinetic Ising model. We quantify the quality of data using effective rank ofthe correlation matrix, and show that data gathered in a out-of-equilibriumregime has a better quality than data gathered in equilibrium for couplingreconstruction. We also propose a matrix-perturbation based method for tun-ing the quality of given data and for removing bad-quality(i.e. redundant)configurations from data.

Data quality for the inverse lsing problem / Decelle, Aurélien; RICCI TERSENGHI, Federico; Zhang, Pan. - In: JOURNAL OF PHYSICS. A, MATHEMATICAL AND THEORETICAL. - ISSN 1751-8113. - STAMPA. - 49:38(2016), p. 384001. [10.1088/1751-8113/49/38/384001]

Data quality for the inverse lsing problem

RICCI TERSENGHI, Federico;
2016

Abstract

There are many methods proposed for inferring parameters of the Ising modelfrom given data, that is a set of configurations generated according to themodel itself. However little attention has been paid until now to the data, e.g.how the data is generated, whether the inference error using one set of datacould be smaller than using another set of data, etc. In this paper we discussthe data quality problem in the inverse Ising problem, using as a benchmarkthe kinetic Ising model. We quantify the quality of data using effective rank ofthe correlation matrix, and show that data gathered in a out-of-equilibriumregime has a better quality than data gathered in equilibrium for couplingreconstruction. We also propose a matrix-perturbation based method for tun-ing the quality of given data and for removing bad-quality(i.e. redundant)configurations from data.
2016
big data; effective rank; inverse Ising problem; parallel dynamics; statistical inference; Statistical and Nonlinear Physics; Statistics and Probability; Modeling and Simulation; Mathematical Physics; Physics and Astronomy (all)
01 Pubblicazione su rivista::01a Articolo in rivista
Data quality for the inverse lsing problem / Decelle, Aurélien; RICCI TERSENGHI, Federico; Zhang, Pan. - In: JOURNAL OF PHYSICS. A, MATHEMATICAL AND THEORETICAL. - ISSN 1751-8113. - STAMPA. - 49:38(2016), p. 384001. [10.1088/1751-8113/49/38/384001]
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/949170
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