Self-exciting spatio-temporal point process models predict the rate of events as a function of
space, time, and the previous history of events. These models naturally capture triggering …

While deterministically predicting the time and location of earthquakes remains impossible,
earthquake forecasting models can provide estimates of the probabilities of earthquakes …

Leveraging large historical data in electronic health record (EHR), we developed Doctor AI,
a generic predictive model that covers observed medical conditions and medication uses …

In this paper we propose an overview of the recent academic literature devoted to the
applications of Hawkes processes in finance. Hawkes processes constitute a particular class …

The concentration of police resources in stable crime hotspots has proven effective in
reducing crime, but the extent to which police can disrupt dynamically changing crime …

We use recent results on statistical analysis of seismicity to present a robust method for
comprehensive detection and analysis of earthquake clusters. The method is based on …

This chapter provides an accessible introduction for point processes, and especially Hawkes
processes, for modeling discrete, inter-dependent events over continuous time. We start by …

Hawkes processes are used in statistical modeling for event clustering and causal inference,
while they also can be viewed as stochastic versions of popular compartmental models used …

Crime hotspot maps are a widely used and successful method of displaying spatial crime
patterns and allocating police resources. However, hotspot maps are often created over a …

Estimating the conditional intensity of a self-exciting point process is particularly challenging
when both exogenous and endogenous effects play a role in clustering. We propose …