Perfect Simulation for the Area-Interaction Point Process

  • Wilfrid S. Kendall
Part of the Lecture Notes in Statistics book series (LNS, volume 128)


Because so many random processes arising in stochastic geometry are quite intractable to analysis, simulation is an important part of the stochastic geometry toolkit. Typically, a Markov point process such as the area-interaction point process is simulated (approximately) as the long-run equilibrium distribution of a (usually reversible) Markov chain such as a spatial birth-and-death process. This is a useful method, but it can be very hard to be precise about the length of simulation required to ensure that the long-run approximation is good. The splendid idea of Propp and Wilson [17] suggests a way forward: they propose a coupling method which delivers exact simulation of equilibrium distributions of (finite-state-space) Markov chains. In this paper their idea is extended to deal with perfect simulation of attractive area-interaction point processes in bounded windows. A simple modification of the basic algorithm is described which provides perfect simulation of the repulsive case as well (which being nonmonotonic might have been thought out of reach). Results from simulations using a C computer program are reported; these confirm the practicality of this approach in both attractive and repulsive cases. The paper concludes by mentioning other point processes which can be simulated perfectly in this way, and by speculating on useful future directions of research. Clearly workers in stochastic geometry should now seek wherever possible to incorporate the Propp and Wilson idea in their simulation algorithms.

Perfect Simulation Spatial Birth-and-Death Processes Area-Interaction Point Process Strauss Point Process Excluded-Area Point Process Poisson Point Process Boolean Model Coupling Markov Chain Monte Carlo 

AMS 1991 Subject Classification

Primary 62M30 Secondary 60G55; 60K35 


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Copyright information

© Springer-Verlag New York, Inc. 1998

Authors and Affiliations

  • Wilfrid S. Kendall
    • 1
  1. 1.StatisticsUniversity of WarwickCoventryUK

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