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.
Optimal low-rank approximations of Bayesian linear inverse problems.SIAM Journal on Scientific Computing, 37(6):A2451–A2487, 2015
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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.