Geostationary satellite (GOES) imagery provides a high temporal resoluti...
The vast majority of modern machine learning targets prediction problems...
Uncertainty quantification is crucial for assessing the predictive abili...
Our goal is to quantify whether, and if so how, spatio-temporal patterns...
Tropical cyclone (TC) intensity forecasts are issued by human forecaster...
Many areas of science make extensive use of computer simulators that
imp...
Conditional density models f(y|x), where x represents a potentially
high...
Prevailing theory contends that tropical cyclones (TCs) are primarily dr...
Prescribed burns are currently the most effective method of reducing the...
Climate models play a crucial role in understanding the effect of
enviro...
Tropical cyclone (TC) intensity forecasts are ultimately issued by human...
Parameter estimation, statistical tests and confidence sets are the
corn...
Tropical cyclones (TCs) rank among the most costly natural disasters in ...
It is well known in astronomy that propagating non-Gaussian prediction
u...
Random forests is a common non-parametric regression technique which per...
Complex phenomena are often modeled with computationally intensive
feed-...
Two-sample testing is a fundamental problem in statistics. Despite its l...
Approximate Bayesian Computation (ABC) is typically used when the likeli...
Random forests is a common non-parametric regression technique which per...
A key question in modern statistics is how to make fast and reliable
inf...