1 citations · 3 across the 4 of their papers we have counts for
4 papers
EarthquakeNPP: A Benchmark for Earthquake Forecasting with Neural Point Processes
Samuel Stockman, Daniel Lawson, Maximilian Werner
For decades, classical point process models, such as the epidemic-type aftershock sequence (ETAS) model, have been widely used for forecasting the event times and locations of eart…
SB-ETAS: using simulation based inference for scalable, likelihood-free inference for the ETAS model of earthquake occurrences
Samuel Stockman, Daniel J. Lawson, Maximilian J. Werner
Performing Bayesian inference for the Epidemic-Type Aftershock Sequence (ETAS) model of earthquakes typically requires MCMC sampling using the likelihood function or estimating the…
Enhancing Uncertain Demand Prediction in Hospitals Using Simple and Advanced Machine Learning
Annie Hu, Samuel Stockman, Xun Wu +3
Early and timely prediction of patient care demand not only affects effective resource allocation but also influences clinical decision-making as well as patient experience. Accura…
Forecasting the 2016-2017 Central Apennines Earthquake Sequence with a Neural Point Process
Samuel Stockman, Daniel J. Lawson, Maximilian J. Werner
Point processes have been dominant in modeling the evolution of seismicity for decades, with the Epidemic Type Aftershock Sequence (ETAS) model being most popular. Recent advances…