Analysis of perfect sampling methods for hard-sphere models

S. B. Moka, S. Juneja, M. R.H. Mandjes

Research output: Contribution to journalConference paperpeer-review

1 Citation (Scopus)

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.

Original languageEnglish
Pages (from-to)69-75
Number of pages7
JournalPerformance Evaluation Review
Volume45
Issue number3
DOIs
Publication statusPublished - Dec 2017
Externally publishedYes
Event35th IFIP International Symposium on Computer Performance, Modeling, Measurements and Evaluation, IFIP WG 7.3 Performance 2017 - New York, United States
Duration: 13 Nov 201717 Nov 2017

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