50 citations · 100 across the 11 of their papers we have counts for
3 papers · 1 filter
A gradient-free subspace-adjusting ensemble sampler for infinite-dimensional Bayesian inverse problems
Matthew M. Dunlop, Georg Stadler
Sampling of sharp posteriors in high dimensions is a challenging problem, especially when gradients of the likelihood are unavailable. In low to moderate dimensions, affine-invaria…
Dimension-Robust MCMC in Bayesian Inverse Problems
Victor Chen, Matthew M. Dunlop, Omiros Papaspiliopoulos +1
The methodology developed in this article is motivated by a wide range of prediction and uncertainty quantification problems that arise in Statistics, Machine Learning and Applied…
Iterative Updating of Model Error for Bayesian Inversion
Daniela Calvetti, Matthew M. Dunlop, Erkki Somersalo +1
In computational inverse problems, it is common that a detailed and accurate forward model is approximated by a computationally less challenging substitute. The model reduction may…