Determinantal point process models and statistical inference

Abstract

Statistical models and methods for determinantal point processes (DPPs) seem largely unexplored. We demonstrate that DPPs provide useful models for the description of repulsive spatial point processes, particularly in the ‘soft-core’ case. Such data are usually modelled by Gibbs point processes, where the likelihood and moment expressions are intractable and simulations are time consuming. We exploit the appealing probabilistic properties of DPPs to develop parametric models, where the likelihood and moment expressions can be easily evaluated and realizations can be quickly simulated. We discuss how statistical inference is conducted using the likelihood or moment properties of DPP models, and we provide freely available software for simulation and statistical inference.

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Cite this paper

@inproceedings{Lavancier2013DeterminantalPP, title={Determinantal point process models and statistical inference}, author={Fr{\'e}d{\'e}ric Lavancier and Jesper M\oller and Ege Rubak}, year={2013} }