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On uncertainty quantification for nonparametric multivariate Hawkes processes

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If you have a question about this talk, please contact Qingyuan Zhao .

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.

The rest of the abstract can be found at https://drive.google.com/file/d/10gjAoVtcY-yyE_qdtEw8vDF4rmqb3BQO/view?usp=sharing

This talk is part of the Statistics series.

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