Efficient EM-Variational Inference for Hawkes Process
This work addresses the need for more flexible modeling in event data analysis, such as in finance or social networks, though it appears incremental as it builds on existing Hawkes process frameworks with nonparametric extensions.
The authors tackled the problem of limited flexibility in classical Hawkes processes by developing a fully Bayesian nonparametric model using Gaussian processes, and their EM-variational inference scheme recovered underlying intensities and kernels without parametric restrictions, outperforming other state-of-the-art methods.
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 generalize it, we present a fully Bayesian nonparametric model, namely Gaussian process modulated Hawkes process and propose an EM-variational inference scheme. In this model, a transformation of Gaussian process is used as a prior on the baseline intensity and triggering kernel. By introducing a latent branching structure, the inference of baseline intensity and triggering kernel is decoupled and the variational inference scheme is embedded into an EM framework naturally. We also provide a series of schemes to accelerate the inference. Results of synthetic and real data experiments show that the underlying baseline intensity and triggering kernel can be recovered without parametric restriction and our Bayesian nonparametric estimation is superior to other state of the arts.