Abstract
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. This paper describes perfect simulation methods of locally stable point processes based on coupling from the past algorithms and provides an intensive simulation study analyzing the behaviour of these techniques under a large variety of practical situations.
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