异常大交叉的布朗占用测度渐近分析
摘要
We prove that the occupation measures of Brownian motions conditioned to have large intersections converge weakly, up to spatial shifts, to the measure whose density is the square of an optimizer of the Gagliardo-Nirenberg inequality. We do so by proving a large deviation principle (LDP) for Brownian occupation measures conditioned either on large self-intersections or large mutual intersections. To this end, we derive a compact LDP for unconditioned Brownian occupation measures, generalizing the work of Mukherjee and Varadhan. We also prove the LDP for Brownian occupation measures tilted by their intersections in the same topology. A key tool of independent interest is an exponentially good approximation of the intersection measure tested against all bounded measurable functions, from which we further get the LDP for the intersection measure of independent Brownian motions.
引用
@article{arxiv.2604.08126,
title = {LLM-Based Data Generation and Clinical Skills Evaluation for Low-Resource French OSCEs},
author = {Tian Huang and Tom Bourgeade and Irina Illina},
journal= {arXiv preprint arXiv:2604.08126},
year = {2026}
}
备注
11 pages, 2 figures, to be published in LREC 2026 proceedings