Bayesian Inference in the Scaling Analysis of Critical Phenomena

Kenji Harada

doi:10.1103/physreve.84.056704

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arXiv · 1102.4149

Bayesian Inference in the Scaling Analysis of Critical Phenomena

Kenji Harada

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Abstract

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.

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Kenji Harada. 2011-11-18. Bayesian Inference in the Scaling Analysis of Critical Phenomena. https://doi.org/10.1103/physreve.84.056704

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