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Frédéric Lavancier (Université de Nantes)

2 October 2015 @ 12:00

 

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Details

Date:
2 October 2015
Time:
12:00
Event Category:

Determinantal point process models and statistical inference

In this talk, I will demonstrate that Determinantal point processes (DPPs) provide useful models for the description of repulsive spatial point processes. Such data are usually modeled by Gibbs point processes, where the likelihood and moment expressions are intractable and simulations are time consuming. I will recall the definition of a DPP and review some of these appealing properties which make DPP models well suited for statistical analysis. In particular the ’most repulsive’ DPP will be characterized. Parametric models of DPPs will be introduced, where the likelihood and moment expressions can be easily evaluated and realizations can be quickly simulated. Some illustrations on real datasets will be presented.

This work has been carried out in collaboration with Jesper Møller and Ege Rubak, Aalborg University. The study of the repulsiveness of DPPs is part of a joint work with Christophe Biscio, University of Nantes.