Abstract
The Renormalization Group (RG) is a set of methods that have been instrumental in tackling problems involving an infinite number of degrees of freedom, such as, for example, in quantum field theory and critical phenomena. What all these methods have in common—which is what explains their success—is that they allow a systematic search for those degrees of freedom that happen to be relevant to the phenomena in question. In the standard approaches the RG transformations are implemented by either coarse graining or through a change of variables. When these transformations are infinitesimal, the formalism can be described as a continuous dynamical flow in a fictitious time parameter. It is generally the case that these exact RG equations are functional diffusion equations. In this paper we show that the exact RG equations can be derived using entropic methods. The RG flow is then described as a form of entropic dynamics of field configurations. Although equivalent to other versions of the RG, in this approach the RG transformations receive a purely inferential interpretation that establishes a clear link to information theory.
Highlights
In this paper we show that the exact Renormalization Group (RG) equations can be derived using entropic methods
The Renormalization Group (RG) is a collection of techniques designed for tackling problems that involve an infinite number of coupled degrees of freedom
In this paper we develop a new approach to the exact RG, derived as an application of entropic methods of inference—and entropic renormalization group
Summary
The Renormalization Group (RG) is a collection of techniques designed for tackling problems that involve an infinite number of coupled degrees of freedom. Caticha and collaborators points in the direction of deploying RG techniques for data analysis [10] Another crucial contribution was Wegner’s realization that the elimination of degrees of freedom is not strictly necessary, that an appropriate change of variables could effectively accomplish the Entropy 2018, 20, 25; doi:10.3390/e20010025 www.mdpi.com/journal/entropy. In [12] the reason why RGs are useful is clear: the changes of variables are such that a classical or saddle-point approximation becomes more accurate, asymptotically approaching the exact result, and offering a way to reach beyond the limitations of perturbation theory. ED had previously been deployed to derive quantum theory as a form of inference both for particles (see, e.g., [26,27]) and for fields [28].
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