Tensorizing maximal correlations
Remi Peyre

TL;DR
This paper develops tensorization theorems for maximal correlations, providing new bounds and decorrelation results for models like the subcritical Ising model, with applications to spatial CLT and spectral gap positivity.
Contribution
It introduces novel tensorization results for maximal correlations under partial independence, improving criteria for bounding correlations between sigma-algebras, with applications to statistical physics models.
Findings
New decorrelation bounds for distant spins in the Ising model.
Application of decorrelation to prove spatial CLT and spectral gap positivity.
An optimal criterion for bounding maximal correlations between sigma-algebras.
Abstract
The maximal (or Hilbertian) correlation coefficient between two random variables X and Y, denoted by \{X:Y\}, is the supremum of the |Corr(f(X),g(Y))| for real measurable functions f, g, where "Corr" denotes Pearson's correlation coefficient. It is a classical result that for independent pairs of variables (X_i,Y_i)_{i\in I}, \{\vec{X}_I:\vec{Y}_I\} is the supremum of the \{X_i:Y_i\}. The main goal of this monograph is to prove similar tensorization results when one only has partial independence between the (X_i,Y_i); more generally, for random variables (X_i)_{i\in I}, (Y_j)_{j\in J}, we will look for bounds on \{\vec{X}_I:\vec{Y}_J\} from bounds on the \{X_i:Y_j\}, i\in I, j\in J. My tensorization theorems will imply new decorrelation results for models of statistical physics exhibiting asymptotic independence, like the subcritical Ising model. I shall prove that for such models,…
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Taxonomy
TopicsMarkov Chains and Monte Carlo Methods · Topological and Geometric Data Analysis · Stochastic processes and statistical mechanics
