Scalable Bayesian Uncertainty Quantification in Imaging Inverse Problems via Convex Optimization
Work
Year: 2019
Type: article
Abstract: We propose a Bayesian uncertainty quantification method for large-scale imaging inverse problems. Our method applies to all Bayesian models that are log-concave, where maximum a posteriori (MAP) estim... more
Source: SIAM Journal on Imaging Sciences
Institutions Actua, Sensors (United States)
Cites: 41
Cited by: 56
Related to: 10
FWCI: 5.854
Citation percentile (by year/subfield): 99.99
Subfield: Computational Mechanics
Field: Engineering
Domain: Physical Sciences
Sustainable Development Goal Peace, justice, and strong institutions
Open Access status: green
Funders Engineering and Physical Sciences Research Council, Engineering and Physical Sciences Research Council
Grant IDS EP/M019306/1, EP/M008843/1