Characterization results and Markov chain Monte Carlo algorithms including exact simulation for some spatial point processes

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Determinantal Point Processes for Machine Learning (2012, methodology): “…pseudolikelihood [Besag, 1977, Besag et al., 1982, Jensen and Moller, 1991, Ripley, 1991], Markov chain Monte Carlo methods [Ripley and Kelly, 1977, Besag and Green, 1993, Häggström et al., 1999, Berthelsen and Møller, 2006], and other approximations [Ogata and Tanemura, 1985, Diggle et al., 1994].”
Perfect simulation using dominating processes on ordered spaces, with application to locally stable point processes (2000, methodology): “For example, in the particular case of the Widom–Rowlinson model we expect the Gibbs sampler in [16] to be more efficient than any of the algorithms considered in the present paper.”
MCMC Using Hamiltonian Dynamics (2011, unknown): “[23], [34], [26], [31], [52], [21], [35], [14], [1]).”
Bayesian Analysis of Markov Point Processes (2006, methodology): “For simulation from the proposal density (5) we suppose that it is straightforward to make simulations from p(θ|θ) but not necessarily from p(x|θ, θ, x); for p(x|θ, θ, x) given by (6) appropriate perfect simulation algorithms [2, 11, 15, 19] are used to avoid convergence questions of straightforward”