Bayesian Analysis of Markov 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].”
Modern Statistics for Spatial Point Processes * (2007, methodology): “Based on perfect simulation (section 9.3) and auxiliary variable MCMC methods (Møller et al., 2006), progress on Bayesian inference for Markov point processes has been made in Berthelsen & Møller (2003, 2004, 2006, 2007).”
Bayesian Inference and Model Assessment for Spatial Point Patterns Using Posterior Predictive Samples (2017, methodology): “Berthelsen and Møller (2006) further study this approach and demonstrate its use for Strauss processes and we employ it below.”