3 papers
cs.LG2026
Gaussian Process Decorrelation for Spatiotemporal Deep Learning-Based Snow Water Equivalent Prediction
Colin Fenster, Adrienne Marshall, Soutir Bandyopadhyay +1
In the Western United States, snowmelt is essential to the agricultural industry in addition to being a key source of municipal drinking water. Consequently, accurate snowpack fore…
stat.ML2025
LatticeVision: Image to Image Networks for Modeling Non-Stationary Spatial Data
Antony Sikorski, Michael Ivanitskiy, Nathan Lenssen +2
In many applications, we wish to fit a parametric statistical model to a small ensemble of spatially distributed random variables ('fields'). However, parameter inference using max…
stat.CO2024
Normalizing Basis Functions: Approximate Stationary Models for Large Spatial Data
Antony Sikorski, Daniel McKenzie, Douglas Nychka
In geostatistics, traditional spatial models often rely on the Gaussian Process (GP) to fit stationary covariances to data. It is well known that this approach becomes computationa…