This article presents a review of computational methods for connectivity inference from
neural activity data derived from multi-electrode recordings or fluorescence imaging. We first …

Many events occur in the world. Some event types are stochastically excited or inhibited—in
the sense of having their probabilities elevated or decreased—by patterns in the sequence …

The Hawkes process is a model for counting the number of arrivals to a system that exhibits
the self-exciting property—that one arrival creates a heightened chance of further arrivals in …

We propose the first multistage intervention framework that tackles fake news in social
networks by combining reinforcement learning with a point process network activity model …

Estimating counterfactual outcomes over time has the potential to unlock personalized
healthcare by assisting decision-makers to answer''what-iF''questions. Existing causal …

Critically ill patients in regular wards are vulnerable to unanticipated adverse events which
require prompt transfer to the intensive care unit (ICU). To allow for accurate prognosis of …

Hawkes process provides an effective statistical framework for analyzing the interactions of
neural spiking activities. Although utilized in many real applications, the classic Hawkes …

The present paper proposes a new methodology to model the lapse risk in life insurance by
integrating the dynamic aspects of policyholders' behaviors and the dependency of the …

Real-world event sequences consist of complex mixtures of different types of events
occurring in time. An event may depend on past events of the same type, as well as, the …

Hawkes processes are a class of simple point processes that are self-exciting and have a
clustering effect, with wide applications in finance, social networks and many other fields …