2 citations · 4 across the 4 of their papers we have counts for
3 papers · 1 filter
Multilevel Monte Carlo estimators for derivative-free optimization under uncertainty
Friedrich Menhorn, Gianluca Geraci, D. Thomas Seidl +3
Optimization is a key tool for scientific and engineering applications, however, in the presence of models affected by uncertainty, the optimization formulation needs to be extende…
Gradient-based data and parameter dimension reduction for Bayesian models: an information theoretic perspective
Ricardo Baptista, Youssef Marzouk, Olivier Zahm
We consider the problem of reducing the dimensions of parameters and data in non-Gaussian Bayesian inference problems. Our goal is to identify an "informed" subspace of the paramet…
Bayesian inverse problems with priors: a Randomize-then-Optimize approach
Zheng Wang, Johnathan M. Bardsley, Antti Solonen +2
Prior distributions for Bayesian inference that rely on the -norm of the parameters are of considerable interest, in part because they promote parameter fields with less regul…