Predictively Oriented Gaussian Process Posteriors
Gaussian Processes (GPs) are a powerful tool for modelling and quantifying uncertainty in functional relationships. However, they require practitioners to make a number of design decisions, such as the choice of the kernel and the observation model. Suboptimal choices can produce misspecified models that do not capture the underlying data generating process. We introduce Predictively Oriented Gaussian Processes (PrO-GPs), which treat predictive uncertainty as the primary inferential target and provide a robust alternative to standard GPs. Although direct computation of a PrO posterior for nonparametric models is intractable, we derive a reduced formulation and practical sampling scheme fo...
| Authors | Callum Lau, Jeremias Knoblauch, Louis Sharrock |
|---|---|
| Primary category | stat.ML |
| Published | 2 October 2026 |
| Last updated at source | 14 October 2026 |
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