50 citations · 100 across the 11 of their papers we have counts for
13 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…
Bayesian neural network priors for edge-preserving inversion
Chen Li, Matthew Dunlop, Georg Stadler
We consider Bayesian inverse problems wherein the unknown state is assumed to be a function with discontinuous structure a priori. A class of prior distributions based on the outpu…
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…
Hyperparameter Estimation in Bayesian MAP Estimation: Parameterizations and Consistency
Matthew M. Dunlop, Tapio Helin, Andrew M. Stuart
The Bayesian formulation of inverse problems is attractive for three primary reasons: it provides a clear modelling framework; means for uncertainty quantification; and it allows f…
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…
How Deep Are Deep Gaussian Processes?
Matthew M. Dunlop, Mark A. Girolami, Andrew M. Stuart +1
Recent research has shown the potential utility of Deep Gaussian Processes. These deep structures are probability distributions, designed through hierarchical construction, which a…
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…
Reconciling Bayesian and perimeter regularization for binary inversion
Oliver R. A. Dunbar, Matthew M. Dunlop, Charles M. Elliott +2
A central theme in classical algorithms for the reconstruction of discontinuous functions from observational data is perimeter regularization via the use of the total variation. On…
Hierarchical Bayesian Level Set Inversion
Matthew M. Dunlop, Marco A. Iglesias, Andrew M. Stuart
The level set approach has proven widely successful in the study of inverse problems for interfaces, since its systematic development in the 1990s. Recently it has been employed in…
MAP Estimators for Piecewise Continuous Inversion
Matthew M. Dunlop, Andrew M. Stuart
We study the inverse problem of estimating a field from data comprising a finite set of nonlinear functionals of , subject to additive noise; we denote this observed data by…
The Bayesian Formulation of EIT: Analysis and Algorithms
Matthew M. Dunlop, Andrew M. Stuart
We provide a rigorous Bayesian formulation of the EIT problem in an infinite dimensional setting, leading to well-posedness in the Hellinger metric with respect to the data. We foc…