7 citations · 10 across the 2 of their papers we have counts for
5 papers
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…
Stability of Gibbs Posteriors from the Wasserstein Loss for Bayesian Full Waveform Inversion
Matthew M. Dunlop, Yunan Yang
Recently, the Wasserstein loss function has been proven to be effective when applied to deterministic full-waveform inversion (FWI) problems. We consider the application of this lo…
Multiplicative noise in Bayesian inverse problems: Well-posedness and consistency of MAP estimators
Matthew M. Dunlop
Multiplicative noise arises in inverse problems when, for example, uncertainty on measurements is proportional to the size of the measurement itself. The likelihood that arises is…
Large Data and Zero Noise Limits of Graph-Based Semi-Supervised Learning Algorithms
Matthew M. Dunlop, Dejan Slepčev, Andrew M. Stuart +1
Scalings in which the graph Laplacian approaches a differential operator in the large graph limit are used to develop understanding of a number of algorithms for semi-supervised le…
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…