EVENT: Seminar

Determinantal point process (DPP) models constitute one of the few non-Poisson point process model classes where we have access to closed form expressions for both the likelihood function and the moments. Furthermore, we have an exact simulation algorithm which avoids the use of Markov chain Monte Carlo methods. In this talk I will define a DPP and briefly review some of these appealing properties which make DPP models well suited for statistical analysis. I will then demonstrate how simulation and statistical inference for DPPs is carried out in practice using software developed in R. Specifically, I will show how we have analyzed several real datasets using this software and the DPP framework. This includes model specification, parameter estimation, simulation from the fitted model, and goodness-of-fit assessment.

Time permitting, I will end the talk with a brief demonstration of how recent developments allow us to extend the software to handle stationary DPPs on a sphere (e.g. the surface of Earth).

The main part of the work has been carried out in collaboration with Jesper Moller from Aalborg University and Fre'de'ric Lavancier from Nantes University, while the final part concerning DPPs on spheres is an ongoing collaboration which also includes Morten Nielsen (Aalborg University).