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
Variational inference for approximate objective priors using neural networks
Nils Baillie, Antoine Van Biesbroeck, Clément Gauchy
In Bayesian statistics, the choice of the prior can have an important influence on the posterior and the parameter estimation, especially when few data samples are available. To li…