# Perfect Sampling for Hard Spheres from Strong Spatial Mixing

arXiv: https://arxiv.org/abs/2305.02450 · HTML: https://mlsanity.com/paper/2305.02450/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 May 2023.

## Built on by (most cited first, 1 of 1)

- [Learning mathsfAC0 under Locally Sampleable Graphical Models](https://mlsanity.com/paper/2607.08303/) (2026)

## Abstract (arXiv)

We provide a perfect sampling algorithm for the hard-sphere model on subsets of ℝd 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.