Quantitative approximate independence for continuous mean field Gibbs measures

Abstract

Many Gibbs measures with mean field interactions are known to be chaotic, in the sense that any collection of k particles in the n-particle system are asymptotically independent, as n → ∞ with k fixed or perhaps k = o(n). This paper quantifies this notion for a class of continuous Gibbs measures on Euclidean space with pairwise interactions, with main examples being systems governed by convex interactions and uniformly convex confinement potentials. The distance between the marginal law of k particles and its limiting product measure is shown to be O((k/n) c∧2 ), with c proportional to the squared temperature. In the high temperature case, this improves upon prior results based on subadditivity of entropy, which yield O(k/n) at best. The bound O((k/n) 2 ) cannot be improved, as a Gaussian example demonstrates. The results are non-asymptotic, and distance is quantified via relative Fisher information, relative entropy, or the squared quadratic Wasserstein metric. The method relies on an a priori functional inequality for the limiting measure, used to derive an estimate for the k-particle distance in terms of the (k + 1)-particle distance.

Stationary solutions and local equations for interacting diffusions on regular trees

“…Convexity is well known to play an important role in the uniqueness on invariant measures (ground states) for mean field models [McC97,CMV03], and a natural open question is if mere uniform convexity of U and K would suffice for uniqueness in the setting of Theorem 1.9. Smallness conditions on the interaction potential, similar to (1.16), are also known ensure uniqueness in mean field models when the interaction is not required to be convex [GLWZ19,Lac21]. 1.3.…”