Kernel method for corrections to scaling release_kppsrk32w5dupjusiwm4wquiy4

by Kenji Harada

Released as a article .

2015  

Abstract

Scaling analysis, in which one infers scaling exponents and a scaling function in a scaling law from given data, is a powerful tool for determining universal properties of critical phenomena in many fields of science. However, there are corrections to scaling in many cases, and then the inference problem becomes ill-posed by an uncontrollable irrelevant scaling variable. We propose a new kernel method based on Gaussian process regression to fix this problem generally. We test the performance of the new kernel method for some example cases. In all cases, when the precision of the example data increases, inference results of the new kernel method correctly converge. Because there is no limitation in the new kernel method for the scaling function even with corrections to scaling, unlike in the conventional method, the new kernel method can be widely applied to real data in critical phenomena.
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Type  article
Stage   accepted
Date   2015-07-07
Version   v2
Language   en ?
arXiv  1410.3622v2
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