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.
Choosing regularization parameters in iterative methods for ill-posed problems.SIAM Journal on Matrix Analysis and Applications, 22(4):1204–1221, 2001
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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.