2 papers
stat.AP2026
Multi-fidelity Gaussian process regression for noisy outputs and non-nested experimental designs: a comparison between the recursive and non-recursive formulations
Nils Baillie, Baptiste Kerleguer, Cyril Feau +1
This paper investigates a recursive formulation of auto-regressive multi-fidelity Gaussian process regression in the challenging setting of noisy and non-nested high- and low-fidel…
stat.ME2025
Bayesian Calibration for Prediction in a Multi-Output Transposition Context
Charlie Sire, Josselin Garnier, Cédric Durantin +3
Numerical simulations are widely used to predict the behavior of physical systems, with Bayesian approaches being particularly well suited for this purpose. However, experimental o…