Likelihood and non-parametric Bayesian MCMC inference for spatial point processes based on perfect simulation and path sampling

We consider the combination of path sampling and perfect simulation in the context of both likelihood inference and non-parametric Bayesian inference for pairwise interaction point processes. Several empirical results based on simulations and analysis of a dataset are presented, and the merits of using perfect simulation are discussed