Combining nonparametric statistical and probabilistic approaches for inference on cloud-of-points data (A05)

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This project combines non-parametric statistical and probabilistic approaches for inference on cloud-of-points data. Such data sets are modeled as point processes, which can be embedded into Hilbert spaces using appropriate reproducing kernels. The project will investigate convergence rates for the associated non-parametric estimators and will extend available results to non-standard probabilistic point process models, such as Gibbsian point processes and point processes over space-time processes, e.g., "clouds of trajectories".