5 papers
Easy Conditioning far beyond Gaussian
Antoine Faul, David Ginsbourger, Ben Spycher
Multivariate Gaussian distributions enjoy Gaussian conditional distributions that makes conditioning easy: conditioning boils down to implementing analytical formulae for condition…
CRPS-Based Targeted Sequential Design with Application in Chemical Space
Lea Friedli, Athénaïs Gautier, Anna Broccard +1
Sequential design of real and computer experiments via Gaussian Process (GP) models has proven useful for parsimonious, goal-oriented data acquisition purposes. In this work, we fo…
An energy-based model approach to rare event probability estimation
Lea Friedli, David Ginsbourger, Arnaud Doucet +1
The estimation of rare event probabilities plays a pivotal role in diverse fields. Our aim is to determine the probability of a hazard or system failure occurring when a quantity o…
Continuous logistic Gaussian random measure fields for spatial distributional modelling
Athénaïs Gautier, David Ginsbourger
We study Spatial Logistic Gaussian Process (SLGP) models for non-parametric estimation of probability density fields using scattered samples of heterogeneous sizes. SLGPs are exami…
Efficient pooling of predictions via kernel embeddings
Sam Allen, David Ginsbourger, Johanna Ziegel
Probabilistic predictions are probability distributions over the set of possible outcomes. Such predictions quantify the uncertainty in the outcome, making them essential for effec…