Analysis of perfect sampling methods for hard-sphere models
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| Publication date | 12-2017 |
| Journal | Performance Evaluation Review |
| Event | 35th IFIP International Symposium on Computer Performance, Modeling, Measurements and Evaluation, IFIP WG 7.3 Performance 2017 |
| Volume | Issue number | 45 | 3 |
| Pages (from-to) | 69-75 |
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| Abstract |
We consider the problem of generating perfect samples from a Gibbs point process, a spatial process that is absolutely continuous w.r.t. a Poisson point process. Examples include area-interaction processes, hard-sphere models and Strauss processes. Traditionally, this is addressed using coupling from the past (CFTP) based methods. We consider acceptance-rejection methods that, unlike the common CFTP methods, do not have the impatient-user bias. Our key contribution is a novel importance sampling based acceptance-rejection methodology for generating perfect samples from Gibbs point processes. We focus on a simpler setting of hard-sphere models in a d-dimensional hypercube that we analyze in an asymptotic regime where the number of spheres generated increases to infinity while the sphere radius decreases to zero at varying rates.
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| Document type | Article |
| Language | English |
| Published at | https://doi.org/10.1145/3199524.3199536 |
| Other links | https://www.scopus.com/pages/publications/85046701068 |
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