Publications (30)
How Deep Are Deep Gaussian Processes?
Matthew M. Dunlop, Mark A. Girolami, Andrew M. Stuart +1
Parameter estimation for macroscopic pedestrian dynamics models from microscopic data
Susana N. Gomes, Andrew M. Stuart, Marie-Therese Wolfram
Geometric MCMC for Infinite-Dimensional Inverse Problems
Alexandros Beskos, Mark Girolami, Shiwei Lan +2
MAP Estimators and Their Consistency in Bayesian Nonparametric Inverse Problems
Masoumeh Dashti, Kody J. H. Law, Andrew M. Stuart +1
Sampling conditioned hypoelliptic diffusions
Martin Hairer, Andrew M. Stuart, Jochen Voss
Analysis of the Gibbs sampler for hierarchical inverse problems
Sergios Agapiou, Johnathan M. Bardsley, Omiros Papaspiliopoulos +1
Iterative Updating of Model Error for Bayesian Inversion
Daniela Calvetti, Matthew M. Dunlop, Erkki Somersalo +1
Gaussian Approximations of Small Noise Diffusions in Kullback-Leibler Divergence
Daniel Sanz-Alonso, Andrew M. Stuart
Hierarchical Bayesian Level Set Inversion
Matthew M. Dunlop, Marco A. Iglesias, Andrew M. Stuart
Uncertainty quantification in graph-based classification of high dimensional data
Andrea L. Bertozzi, Xiyang Luo, Andrew M. Stuart +1
Uncertainty quantification and weak approximation of an elliptic inverse problem
Masoumeh Dashti, Andrew M. Stuart
Dimension-Robust MCMC in Bayesian Inverse Problems
Victor Chen, Matthew M. Dunlop, Omiros Papaspiliopoulos +1
Posterior Contraction Rates for the Bayesian Approach to Linear Ill-Posed Inverse Problems
Sergios Agapiou, Stig Larsson, Andrew M. Stuart
The Ensemble Kalman Filter for Inverse Problems
Marco A. Iglesias, Kody J. H. Law, Andrew M. Stuart
The Bayesian Approach To Inverse Problems
Masoumeh Dashti, Andrew M. Stuart
Spectral gaps for a Metropolis-Hastings algorithm in infinite dimensions
Martin Hairer, Andrew M. Stuart, Sebastian J. Vollmer
Evaluation of Gaussian approximations for data assimilation in reservoir models
Marco A. Iglesias, Kody J. H. Law, Andrew M. Stuart
Filter Based Methods For Statistical Linear Inverse Problems
Marco A. Iglesias, Kui Lin, Shuai Lu +1
Large Data and Zero Noise Limits of Graph-Based Semi-Supervised Learning Algorithms
Matthew M. Dunlop, Dejan SlepÄev, Andrew M. Stuart +1
A Function Space HMC Algorithm With Second Order Langevin Diffusion Limit
Michela Ottobre, Natesh S. Pillai, Frank J. Pinski +1
Gradient Flow from a Random Walk in Hilbert Space
Natesh S. Pillai, Andrew M. Stuart, Alexandre H. Thiery
Optimal scaling and diffusion limits for the Langevin algorithm in high dimensions
Natesh S. Pillai, Andrew M. Stuart, Alexandre H. Thiéry
Bayesian Posterior Contraction Rates for Linear Severely Ill-posed Inverse Problems
Sergios Agapiou, Andrew M. Stuart, Yuan-Xiang Zhang
Gaussian approximations for transition paths in Brownian dynamics
Yulong Lu, Andrew M. Stuart, Hendrik Weber
A Bayesian Level Set Method for Geometric Inverse Problems
Marco A. Iglesias, Yulong Lu, Andrew M. Stuart
Inverse optimal transport
Andrew M. Stuart, Marie-Therese Wolfram
Diffusion limits of the random walk Metropolis algorithm in high dimensions
Jonathan C. Mattingly, Natesh S. Pillai, Andrew M. Stuart
Determining White Noise Forcing From Eulerian Observations in the Navier Stokes Equation
Viet Ha Hoang, Kody J. H. Law, Andrew M. Stuart
Gaussian Approximations for Probability Measures on $\mathbf{R}^d$
Yulong Lu, Andrew M. Stuart, Hendrik Weber
Well-Posed Bayesian Geometric Inverse Problems Arising in Subsurface Flow
Marco A. Iglesias, Kui Lin, Andrew M. Stuart