English

High-dimensional Laplace asymptotics up to the concentration threshold

Classical Analysis and ODEs 2026-03-13 v2 Probability Statistics Theory Statistics Theory

Abstract

We study high-dimensional Laplace-type integrals I(λ):=(λ/2π)d/2Rdg(x)eλf(x)dxI(\lambda):=(\lambda/2\pi)^{d/2}\int_{\mathbb R^d} g(x)e^{-\lambda f(x)}dx in the regime where both dd and λ\lambda are large. Existing rigorous Laplace-expansion results in growing dimension are largely confined to the "Gaussian-approximation" regime d2/λ0d^2/\lambda\to0, which excludes many practically relevant settings that lie beyond this threshold but still satisfy the concentration condition d/λ0d/\lambda\to0. We close this gap by deriving an explicit asymptotic expansion for logI(λ)\log I(\lambda) with quantitative remainder bounds that remain valid throughout this intermediate region, arbitrarily close to the concentration threshold. Fix L1L\ge1 and assume that, in a neighborhood of the global minimizer of ff, the operator norms of derivatives of ff and gg are bounded independently of d,λd,\lambda up to orders 2L+22L+2 and 2L2L, respectively. Assuming also some mild global growth conditions, we prove logI(λ)=k=1L1bk(f,g)λk+O(dL+1/λL),dL+1/λL0,\log I(\lambda)=\sum_{k=1}^{L-1} b_k(f,g)\lambda^{-k}+O(d^{L+1}/\lambda^L), \qquad d^{L+1}/\lambda^L\to0, with coefficients satisfying bk(f,g)=O(dk+1)b_k(f,g)=O(d^{k+1}). Moreover, the bk(f,g)b_k(f,g) coincide with the coefficients from the formal cumulant expansion of logI(λ)\log I(\lambda). We also study computation for concentrating densities π(x)eλf(x)\pi(x)\propto e^{-\lambda f(x)}. For smooth observables gg, our expansion yields closed-form, analytic approximations of EXπ[g(X)]\mathbb E_{X\sim\pi}[g(X)]. For sampling, we construct explicit polynomial transports xLx_L such that πL:=(xL)#N(0,λ1Id)\pi_L:=(x_L)_\# N(0,\lambda^{-1}I_d) satisfies TV(π,πL)dL+1/λL\mathrm{TV}(\pi,\pi_L)\lesssim d^{L+1}/\lambda^L for L=1,2,3,L=1,2,3,\dots, yielding an accurate procedure arbitrarily close to the concentration threshold d=o(λ)d=o(\lambda).

Keywords

Cite

@article{arxiv.2602.23151,
  title  = {High-dimensional Laplace asymptotics up to the concentration threshold},
  author = {Alexander Katsevich and Anya Katsevich},
  journal= {arXiv preprint arXiv:2602.23151},
  year   = {2026}
}

Comments

Change from v1: added new result on normalizing flow style posterior approximation

R2 v1 2026-07-01T10:54:07.311Z