English

Poincar\'e Inequality for Dirichlet Distributions and Infinite-Dimensional Generalizations

Probability 2015-09-07 v2

Abstract

For any N2N\ge 2 and a˚:=(a˚1,,a˚N+1)(0,)N+1\aa:=(\aa_1,\cdots, \aa_{N+1})\in (0,\infty)^{N+1}, let μa˚(N)\mu^{(N)}_{\aa} be the corresponding Dirichlet distribution on \DD:={x=(xi)1iN[0,1]N: 1iNxi1}.\DD:= \big\{ x=(x_i)_{1\le i\le N}\in [0,1]^N:\ \sum_{1\le i\le N} x_i\le 1\big\}. We prove the Poincar\'e inequality \mu^{(N)}_{\aa}(f^2)\le \ff 1 {\aa_{N+1}} \int_{\DD}\Big\{\Big(1-\sum_{1\le i\le N} x_i\Big) \sum_{n=1}^N x_n(\pp_n f)^2\Big\}\mu^{(N)}_\aa(\d x)+\mu^{(N)}_{\aa}(f)^2,\ f\in C^1(\DD) and show that the constant \ff1a˚N+1\ff 1 {\aa_{N+1}} is sharp. Consequently, the associated diffusion process on \DD\DD converges to μa˚(N)\mu^{(N)}_{\aa} in L2(μa˚(N))L^2(\mu^{(N)}_{\aa}) at the exponentially rate a˚N+1\aa_{N+1}. The whole spectrum of the generator is also characterized. Moreover, the sharp Poincar\'e inequality is extended to the infinite-dimensional setting, and the spectral gap of the corresponding discrete model is derived.

Keywords

Cite

@article{arxiv.1504.02829,
  title  = {Poincar\'e Inequality for Dirichlet Distributions and Infinite-Dimensional Generalizations},
  author = {L. Miclo and S. Feng and F. -Y. Wang},
  journal= {arXiv preprint arXiv:1504.02829},
  year   = {2015}
}

Comments

21 pages

R2 v1 2026-06-22T09:14:26.535Z