Spatio-Temporal Hawkes 工艺的灵活而可扩展的贝叶斯模型

Flexible and Scalable Bayesian Modelling of Spatio-Temporal Hawkes Processes

Existing spatio-temporal Hawkes process models typically rely on either parametric or semiparametric assumptions, limiting the model's ability to capture complex endogenous and exogenous event dynamics. We propose a fully Bayesian nonparametric framework for spatio-temporal Hawkes processes using additive Gaussian processes for the prior distributions on the background rate and the triggering kernel. This additive structure enhances interpretability by decoupling temporal and spatial effects whi