Name
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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Year | Venue | Field |
|---|---|---|
2017 | SIGMETRICS Performance Evaluation Review | Applied mathematics,Importance sampling,Absolute continuity,Coupling from the past,Point process,Infinity,SPHERES,Poisson point process,Mathematics,Hypercube,Distributed computing |
DocType | Volume | Issue |
Journal | 45 | 3 |
Citations | PageRank | References |
0 | 0.34 | 3 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Sarat Babu Moka | 1 | 2 | 0.80 |
| Sandeep Juneja | 2 | 4 | 2.49 |
| Michel Mandjes | 3 | 534 | 73.65 |