Advances in Applied Probability

Perfect simulation of some point processes for the impatient user

Elke Thönnes

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Abstract

Recently Propp and Wilson [14] have proposed an algorithm, called coupling from the past (CFTP), which allows not only an approximate but perfect (i.e. exact) simulation of the stationary distribution of certain finite state space Markov chains. Perfect sampling using CFTP has been successfully extended to the context of point processes by, amongst other authors, Häggström et al. [5]. In [5] Gibbs sampling is applied to a bivariate point process, the penetrable spheres mixture model [19]. However, in general the running time of CFTP in terms of number of transitions is not independent of the state sampled. Thus an impatient user who aborts long runs may introduce a subtle bias, the user impatience bias. Fill [3] introduced an exact sampling algorithm for finite state space Markov chains which, in contrast to CFTP, is unbiased for user impatience. Fill's algorithm is a form of rejection sampling and similarly to CFTP requires sufficient monotonicity properties of the transition kernel used. We show how Fill's version of rejection sampling can be extended to an infinite state space context to produce an exact sample of the penetrable spheres mixture process and related models. Following [5] we use Gibbs sampling and make use of the partial order of the mixture model state space. Thus we construct an algorithm which protects against bias caused by user impatience and which delivers samples not only of the mixture model but also of the attractive area-interaction and the continuum random-cluster process.

Article information

Source
Adv. in Appl. Probab. Volume 31, Number 1 (1999), 69-87.

Dates
First available in Project Euclid: 21 August 2002

Permanent link to this document
http://projecteuclid.org/euclid.aap/1029954267

Digital Object Identifier
doi:10.1239/aap/1029954267

Mathematical Reviews number (MathSciNet)
MR1699662

Zentralblatt MATH identifier
0979.60062

Subjects
Primary: 60J10: Markov chains (discrete-time Markov processes on discrete state spaces)
Secondary: 68U20: Simulation [See also 65Cxx] 60G57: Random measures 60D05: Geometric probability and stochastic geometry [See also 52A22, 53C65]

Keywords
Markov chain Monte Carlo perfect simulation point processes user impatience bias Gibbs sampler penetrable spheres mixture process area-interaction process continuum random-cluster model

Citation

Thönnes, Elke. Perfect simulation of some point processes for the impatient user. Adv. in Appl. Probab. 31 (1999), no. 1, 69--87. doi:10.1239/aap/1029954267. http://projecteuclid.org/euclid.aap/1029954267.


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