English

Wasserstein convergence rates of increasingly concentrating probability measures

Probability 2023-12-11 v2

Abstract

For  ⁣:Rd[0,)\ell\colon \mathbb{R}^d \to [0,\infty) we consider the sequence of probability measures (μn)nN\left(\mu_n\right)_{n \in \mathbb{N}}, where μn\mu_n is determined by a density that is proportional to exp(n)\exp(-n\ell). We allow for infinitely many global minimal points of \ell, as long as they form a finite union of compact manifolds. In this scenario, we show estimates for the pp-Wasserstein convergence of (μn)nN\left(\mu_n\right)_{n \in \mathbb{N}} to its limit measure. Imposing regularity conditions we obtain a speed of convergence of n1/(2p)n^{-1/(2p)} and adding a further technical assumption, we can improve this to a pp-independent rate of 1/21/2 for all orders pNp\in\mathbb{N} of the Wasserstein distance.

Keywords

Cite

@article{arxiv.2207.08551,
  title  = {Wasserstein convergence rates of increasingly concentrating probability measures},
  author = {Mareike Hasenpflug and Daniel Rudolf and Björn Sprungk},
  journal= {arXiv preprint arXiv:2207.08551},
  year   = {2023}
}

Comments

29 pages, 3 Figures, accepted for publication in Ann. Appl. Probab