1 citations · 1 across the 3 of their papers we have counts for
3 papers
Gaussian mixture models for model improvement
Paolo Villani, Daniel Andrés Arcones, Jörg F. Unger +1
Modeling complex physical systems such as they arise in civil engineering applications requires finding a trade-off between physical fidelity and practicality. Consequently, deviat…
Posterior sampling with Adaptive Gaussian Processes in Bayesian parameter identification
Paolo Villani, Daniel Andrés-Arcones, Jörg F. Unger +1
Posterior sampling by Monte Carlo methods provides a more comprehensive solution approach to inverse problems than computing point estimates such as the maximum posterior using opt…
Adaptive Gaussian Process Regression for Bayesian inverse problems
Paolo Villani, Jörg Unger, Martin Weiser
We introduce a novel adaptive Gaussian Process Regression (GPR) methodology for efficient construction of surrogate models for Bayesian inverse problems with expensive forward mode…