Determinantal point process models and statistical inference

International audienceStatistical models and methods for determinantal point processes (DPPs) seemlargely unexplored. We demonstrate that DPPs provide useful models for the description ofspatial point pattern data sets where nearby points repel each other. Such data are usually modelledby Gibbs point processes, where the likelihood and moment expressions are intractableand simulations are time consuming.We exploit the appealing probabilistic properties of DPPsto develop parametric models, where the likelihood and moment expressions can be easilyevaluated and realizations can be quickly simulated. We discuss how statistical inference isconducted by using the likelihood or moment properties of DPP models, and we provide freelyavailable software for simulation and statistical inference