32 citations · 47 across the 9 of their papers we have counts for
10 papers
A hybrid Gibbs sampler for edge-preserving tomographic reconstruction with uncertain view angles
Felipe Uribe, Johnathan M. Bardsley, Yiqiu Dong +2
In computed tomography, data consist of measurements of the attenuation of X-rays passing through an object. The goal is to reconstruct the linear attenuation coefficient of the ob…
Optimization-Based MCMC Methods for Nonlinear Hierarchical Statistical Inverse Problems
Johnathan Bardsley, Tiangang Cui
In many hierarchical inverse problems, not only do we want to estimate high- or infinite-dimensional model parameters in the parameter-to-observable maps, but we also have to estim…
Scalable optimization-based sampling on function space
Johnathan Bardsley, Tiangang Cui, Youssef Marzouk +1
Optimization-based samplers such as randomize-then-optimize (RTO) [2] provide an efficient and parallellizable approach to solving large-scale Bayesian inverse problems. These meth…
Semivariogram methods for modeling Whittle-Matérn priors in Bayesian inverse problems
Richard D. Brown, Johnathan M. Bardsley, Tiangang Cui
We present a new technique, based on semivariogram methodology, for obtaining point estimates for use in prior modeling for solving Bayesian inverse problems. This method requires…
Efficient Marginalization-based MCMC Methods for Hierarchical Bayesian Inverse Problems
Arvind K. Saibaba, Johnathan Bardsley, D. Andrew Brown +1
Hierarchical models in Bayesian inverse problems are characterized by an assumed prior probability distribution for the unknown state and measurement error precision, and hyper-pri…
Parameter and Uncertainty Estimation for Dynamical Systems Using Surrogate Stochastic Processes
M. Chung, M. Binois, R. B. Gramacy +3
Inference on unknown quantities in dynamical systems via observational data is essential for providing meaningful insight, furnishing accurate predictions, enabling robust control,…