Optimal low-rank posterior covariance approximation in linear Gaussian inverse problems on Hilbert spaces

For linear inverse problems with Gaussian priors and Gaussian observation noise, the posterior is Gaussian, with mean and covariance determined by the conditioning formula. The covariance is the central object for uncertainty quantification, as it encodes the variability of the posterior distributio