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题名: Data quality for the inverse Ising problem
作者: Decelle, A ;  Ricci-Tersenghi, F ;  Zhang, P
刊名: JOURNAL OF PHYSICS A-MATHEMATICAL AND THEORETICAL
出版日期: 2016
卷号: 49, 期号:38, 页码:384001
关键词: statistical inference ;  inverse Ising problem ;  big data ;  parallel dynamics ;  effective rank
学科分类: Physics
DOI: http://dx.doi.org/10.1088/1751-8113/49/38/384001
通讯作者: Ricci-Tersenghi, F (reprint author), Univ Roma La Sapienza, Dipartimento Fis, Ist Nazl Fis Nucl, Sez Roma1, Piazzale Aldo Moro 5, I-00185 Rome, Italy. ;  Ricci-Tersenghi, F (reprint author), Univ Roma La Sapienza, CNR Nanotec, I-00185 Rome, Italy.
文章类型: Article
英文摘要: There are many methods proposed for inferring parameters of the Ising model from given data, that is a set of configurations generated according to the model 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 data could be smaller than using another set of data, etc. In this paper we discuss the data quality problem in the inverse Ising problem, using as a benchmark the kinetic Ising model. We quantify the quality of data using effective rank of the correlation matrix, and show that data gathered in a out-of-equilibrium regime has a better quality than data gathered in equilibrium for coupling reconstruction. We also propose a matrix-perturbation based method for tuning the quality of given data and for removing bad-quality (i.e. redundant) configurations from data.
类目[WOS]: Physics, Multidisciplinary ;  Physics, Mathematical
关键词[WOS]: MODEL
收录类别: SCI
语种: 英语
Citation statistics: 
内容类型: 期刊论文
URI标识: http://ir.itp.ac.cn/handle/311006/21526
Appears in Collections:理论物理所2016年知识产出_期刊论文

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Recommended Citation:
Decelle, A,Ricci-Tersenghi, F,Zhang, P. Data quality for the inverse Ising problem[J]. JOURNAL OF PHYSICS A-MATHEMATICAL AND THEORETICAL,2016,49(38):384001.
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