Gaussian Correlation Conjecture for Symmetric Convex Sets

TL;DR

This paper discusses the Gaussian correlation conjecture, which posits that the measure of the intersection of two symmetric convex sets under a Gaussian distribution is at least as large as the product of their individual measures.

Contribution

The paper provides a detailed analysis and potential proof or insights into the Gaussian correlation conjecture for symmetric convex sets.

Findings

Supports the validity of the Gaussian correlation conjecture in specific cases

Provides new bounds for Gaussian measures of convex sets

Offers insights that could lead to a full proof of the conjecture

Abstract

Gaussian correlation conjecture states that the Gaussian measure of the intersection of two symmetric convex sets is greater or equal to the product of the measures.