Accurate and Reliable Uncertainty Estimates for Deterministic Predictions Extensions to Under and Overpredictions
Computational models support high-stakes decisions across engineering and science, and practitioners increasingly seek probabilistic predictions to quantify uncertainty in such models. Existing approaches generate predictions either by sampling input parameter distributions or by augmenting deterministic outputs with uncertainty representations, including distribution-free and distributional methods. However, sampling-based methods are often computationally prohibitive for real-time applications, and many existing uncertainty representations either ignore input dependence or rely on restrictive Gaussian assumptions that fail to capture asymmetry and heavy-tailed behavior. Therefore, we ex...
| Authors | Rileigh Bandy, Enrico Camporeale, Andong Hu et al. |
|---|---|
| Primary category | cs.CE |
| Published | 9 April 2026 |
| Last updated at source | 9 April 2026 |
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