Abstract
Spatial point process models provide a large variety of complex patterns to model particular clustered situations. Due to model complexity, spatial statistics often relies on simulation methods. Probably the most common such method is Markov chain Monte Carlo (MCMC) which draws approximate samples of the target distribution as the equilibrium distribution of a Markov chain. Perfect simulation methods are MCMC algorithms which ensure that the exact target distribution is sampled. In this paper we focus on point field models that have been used as particular models of galaxy clustering in both cosmology and spatial statistics. We present simulation and estimation techniques for these models and analyze by an extensive simulation study their flexibility for cluster modeling, under a large variety of practical situations.
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