Parsimonious Description of Generalized Gibbs Measures : Decimation of the 2d-Ising Model
Arnaud Le Ny

TL;DR
This paper characterizes the decimation of the 2D Ising model within the generalized Gibbs framework, providing a detailed description of its almost Gibbsian properties and potential decay behavior.
Contribution
It offers a complete characterization of the decimated 2D Ising model as an almost Gibbsian measure with a quenched correlation decay potential, advancing the understanding of renormalized measures.
Findings
Decimated measures are almost Gibbsian with a well-defined potential.
The potential exhibits exponential decay beyond a configuration-dependent length.
The results integrate decimated measures into the framework of parsimonious random fields.
Abstract
In this paper, we detail and complete the existing characterizations of the decimation of the Ising model on in the generalized Gibbs context. We first recall a few features of the Dobrushin program of restoration of Gibbsianness and present the construction of global specifications consistent with the extremal decimated measures. We use them to consider these renormalized measures as almost Gibbsian measures and to precise its convex set of DLR measures. We also recall the weakly Gibbsian description and complete it using a potential that admits a quenched correlation decay, i.e. a well-defined configuration-dependent length beyond which this potential decays exponentially. We use these results to incorporate these decimated measures in the new framework of parsimonious random fields that has been recently developed to investigate probability aspects related to neurosciences.
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsStochastic processes and statistical mechanics · Markov Chains and Monte Carlo Methods · Theoretical and Computational Physics
