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, such as Markov chain Monte Carlo (MCMC) which draws approximate samples of the target distribution as the equilibrium distribution of a Markov chain. 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 thus select several cluster models and focus on the cluster random model. We provide an extensive simulation study to analyze their flexibility for cluster modelling, under a large variety of practical situations.