Perfect Sampling for Hard Spheres from Strong Spatial Mixing
Fuente:
arXiv
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| Autores principales: | , , , |
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| Formato: | Preprint |
| Publicado: |
2023
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866913474723971072 |
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| author | Anand, Konrad Göbel, Andreas Pappik, Marcus Perkins, Will |
| author_facet | Anand, Konrad Göbel, Andreas Pappik, Marcus Perkins, Will |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2305_02450 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Perfect Sampling for Hard Spheres from Strong Spatial Mixing Anand, Konrad Göbel, Andreas Pappik, Marcus Perkins, Will Data Structures and Algorithms Mathematical Physics Probability 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. |
| title | Perfect Sampling for Hard Spheres from Strong Spatial Mixing |
| topic | Data Structures and Algorithms Mathematical Physics Probability |
| url | https://arxiv.org/abs/2305.02450 |