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Kernel method for corrections to scaling
2015
Physical Review E
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
doi:10.1103/physreve.92.012106
pmid:26274124
fatcat:lad245xnqje4lnivismypgvwj4