Temporal point processes (TPP) are probabilistic generative models for continuous-time
event sequences. Neural TPPs combine the fundamental ideas from point process literature …

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 …

SUMMARY The Collaboratory for the Study of Earthquake Predictability (CSEP) is an
international effort to evaluate earthquake forecast models prospectively. In CSEP, one way …

Reported gang-related violent crimes in Los Angeles, California, from 1/1/14 to 12/31/17 are
modeled using spatial–temporal marked Hawkes point processes with covariates. We …

In view of the growing importance of stochastic earthquake modeling in disaster preparation,
the present study introduces a new family of exponentiated Weibull distribution and …

ABSTRACT The Collaboratory for the Study of Earthquake Predictability (CSEP) is an
international effort to evaluate probabilistic earthquake forecasting models. CSEP provides …

Event sequence, where each event is associated with a marker and a timestamp, is
increasingly ubiquitous in various applications. Accordingly, event forecasting emerges to …

In honor of our dear departed friends Yan Kagan, Dave Jackson, and Ilya Zaliapin, we
propose a selection of broad questions regarding earthquake forecasting and especially the …

Attention guides our gaze to fixate the proper location of the scene and holds it in that
location for the deserved amount of time given current processing demands, before shifting …

Many point process models have been proposed for describing and forecasting earthquake
occurrences in seismically active zones such as California, but the problem of how best to …