硬球模型的强空间混合完美采样
Perfect Sampling for Hard Spheres from Strong Spatial Mixing
AI总结：
提出一种基于强空间混合的硬球模型完美采样算法，期望运行时间与体积线性，优于已知近似采样器，并推广至有限范围排斥势的吉布斯点过程。
摘要：
我们提出了一种用于$\mathbb{R}^d$子集上硬球模型的完美采样算法，在强空间混合假设下，其期望运行时间与体积呈线性关系。已有大量完美和近似采样算法被设计用于从硬球模型中进行采样，而我们的完美采样算法在一系列参数范围内是高效的，此前这些参数范围仅已知高效的近似采样器，并且我们的算法比这些已知的近似方法更快。我们的方法还扩展到通过有限范围、排斥势相互作用的吉布斯点过程的更一般设置。
英文摘要：
We provide a perfect sampling algorithm for the hard-sphere model on subsets of $\mathbb{R}^d$ with expected running time linear in the volume under the assumption of strong spatial mixing. A large number of perfect and approximate sampling algorithms have been devised to sample from the hard-sphere model, and our perfect sampling algorithm is efficient for a range of parameters for which only efficient approximate samplers were previously known and is faster than these known approximate approaches. Our methods also extend to the more general setting of Gibbs point processes interacting via finite-range, repulsive potentials.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
