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stat.CO2021
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…
stat.CO2020
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…
stat.CO2019
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…