Multivariate Hawkes processes form a class of point processes describing self and inter exciting/inhibiting processes. There is now a renewed interest of such processes in applied domains and in machine learning, but there exists only limited theory about inference in such models apart from parametric models. After reviewing results on convergence rates for Bayesian nonparametric approaches to such models, I will present new results on uncertainty quantification for important functionals.

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