3 papers
stat.ML2020
Additive Poisson Process: Learning Intensity of Higher-Order Interaction in Stochastic Processes
Simon Luo, Feng Zhou, Lamiae Azizi +1
We present the Additive Poisson Process (APP), a novel framework that can model the higher-order interaction effects of the intensity functions in stochastic processes using lower…
stat.AP2019
Fast Multi-resolution Segmentation for Nonstationary Hawkes Process Using Cumulants
Feng Zhou, Zhidong Li, Xuhui Fan +3
The stationarity is assumed in vanilla Hawkes process, which reduces the model complexity but introduces a strong assumption. In this paper, we propose a fast multi-resolution segm…
cs.LG2019
Efficient EM-Variational Inference for Hawkes Process
Feng Zhou, Zhidong Li, Xuhui Fan +3
In classical Hawkes process, the baseline intensity and triggering kernel are assumed to be a constant and parametric function respectively, which limits the model flexibility. To…