Hawkes Process Inference With Missing Data


  • Christian Shelton University of California, Riverside
  • Zhen Qin University of California, Riverisde
  • Chandini Shetty University of California, Riverside


point process, Hawkes process, MCMC inference


A multivariate Hawkes process is a class of marked point processes: A sample consists of a finite set of events of unbounded random size; each event has a real-valued time and a discrete-valued label (mark). It is self-excitatory: Each event causes an increase in the rate of other events (of either the same or a different label) in the (near) future. Prior work has developed methods for parameter estimation from complete samples. However, just as unobserved variables can increase the modeling power of other probabilistic models, allowing unobserved events can increase the modeling power of point processes. In this paper we develop a method to sample over the posterior distribution of unobserved events in a multivariate Hawkes process. We demonstrate the efficacy of our approach, and its utility in improving predictive power and identifying latent structure in real-world data.




How to Cite

Shelton, C., Qin, Z., & Shetty, C. (2018). Hawkes Process Inference With Missing Data. Proceedings of the AAAI Conference on Artificial Intelligence, 32(1). Retrieved from https://ojs.aaai.org/index.php/AAAI/article/view/12116



AAAI Technical Track: Reasoning under Uncertainty