The paper introduces a data-informed subspace method with quotient-space Golub-Kahan bidiagonalization and integrated empirical Bayes for efficient posterior approximation in high-dimensional linear inverse problems.
Hybrid and iteratively reweighted regularization by unbiased predictive risk and weighted GCV for projected systems.SIAM Journal on Scientific Computing, 39(2):B221–B243, 2017
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
math.NA 1years
2026 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
-
Data-informed posterior approximation for Bayesian linear inverse problems
The paper introduces a data-informed subspace method with quotient-space Golub-Kahan bidiagonalization and integrated empirical Bayes for efficient posterior approximation in high-dimensional linear inverse problems.