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stat.CO2026
A new framework for non-stationary spatio-temporal data fusion of multi-fidelity models
Pietro Colombo, Fabio Sigrist, Claire Miller +3
We propose a new scalable framework for spatio-temporal data fusion with multi-fidelity Gaussian processes (MFGPs) that enables fully likelihood-based inference for both stationary…
stat.CO2025
An accuracy-runtime trade-off comparison of scalable Gaussian process approximations for spatial data
Filippo Rambelli, Fabio Sigrist
Gaussian processes (GPs) are flexible, probabilistic, nonparametric models widely used in fields such as spatial statistics and machine learning. A drawback of Gaussian processes i…