6 citations · 7 across the 7 of their papers we have counts for
7 papers
Gaussian Process Modeling with Genotype x Environment Kernels for Wheat Performance Prediction
Lea Friedli, Tim Steinert, Nathalie Wuyts +5
Optimizing wheat variety selection for high performance in different environmental conditions is critical for reliable food production and stable incomes for growers. We employ a s…
Sensitivity measures for engineering and environmental decision support
Daniel Straub, Wolfgang Betz, Mara Ruf +4
Information value, a measure for decision sensitivity, can provide essential information in engineering and environmental assessments. It quantifies the potential for improved deci…
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…
Rare event probability estimation for groundwater inverse problems with a two-stage Sequential Monte Carlo approach
Lea Friedli, Niklas Linde
Bayesian inversions followed by estimations of rare event probabilities are often needed to analyse groundwater hazards. Instead of focusing on the posterior distribution of model…
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…
Lithological Tomography with the Correlated Pseudo-Marginal Method
Lea Friedli, Niklas Linde, David Ginsbourger +1
We consider lithological tomography in which the posterior distribution of (hydro)geological parameters of interest is inferred from geophysical data by treating the intermediate g…
Area-covering postprocessing of ensemble precipitation forecasts using topographical and seasonal conditions
Lea Friedli, David Ginsbourger, Jonas Bhend
Probabilistic weather forecasts from ensemble systems require statistical postprocessing to yield calibrated and sharp predictive distributions. This paper presents an area-coverin…