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Bayesian Inference in the Scaling Analysis of Critical Phenomena

By Kenji Harada


To determine the universality class of critical phenomena, we propose a method of statistical inference in the scaling analysis of critical phenomena. The method is based on Bayesian statistics, most specifically, the Gaussian process regression. It assumes only the smoothness of a scaling function, and it does not need a form. We demonstrate this method for the finite-size scaling analysis of the Ising models on square and triangular lattices. Near the critical point, the method is comparable in accuracy to the least-square method. In addition, it works well for data to which we cannot apply the least-square method with a polynomial of low degree. By comparing the data on triangular lattices with the scaling function inferred from the data on square lattices, we confirm the universality of the finite-size scaling function of the two-dimensional Ising model.Comment: 7 pages, 8 figures, and 1 tabl

Topics: Condensed Matter - Statistical Mechanics, Condensed Matter - Strongly Correlated Electrons, Physics - Computational Physics, Physics - Data Analysis, Statistics and Probability
Year: 2011
DOI identifier: 10.1103/PhysRevE.84.056704
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