Likelihood-based inference for Matérn type-III repulsive point processes

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Determinantal Point Processes for Machine Learning (2012, methodology): “If the set of locations is further restricted at each step to those found in an initially selected Poisson set Y , then it is equivalent to a Matérn Type III process [Huber and Wolpert, 2009]; it therefore shares the same computational burdens.”
Stochastic Geometry for Modeling, Analysis, and Design of Multi-Tier and Cognitive Cellular Wireless Networks: A Survey (2013, background): “As a result, we may be able to get only approximate solutions or tight bound on the SINR distribution.”
Perfect simulation and moment properties for the Matérn type III process (2010, methodology): “To support inference about λ and R, when only a finite point pattern {zi}i∈I ⊂ W is observed and modeled by a finite version of the Matérn III process within W , Huber and Wolpert (2009) developed a perfect simulation algorithm of the latent times {ti}i∈I and the removed marked points but without ac”