3 papers
stat.ME2024
Deep Gaussian Process Emulation and Uncertainty Quantification for Large Computer Experiments
Faezeh Yazdi, Derek Bingham, Daniel Williamson
Computer models are used as a way to explore complex physical systems. Stationary Gaussian process emulators, with their accompanying uncertainty quantification, are popular surrog…
stat.ME2023
Feature calibration for computer models
Wenzhe Xu, Daniel B. Williamson, Frederic Hourdin +1
Computer model calibration involves using partial and imperfect observations of the real world to learn which values of a model's input parameters lead to outputs that are consiste…
cs.AI2023
On the meaning of uncertainty for ethical AI: philosophy and practice
Cassandra Bird, Daniel Williamson, Sabina Leonelli
Whether and how data scientists, statisticians and modellers should be accountable for the AI systems they develop remains a controversial and highly debated topic, especially give…