A joint covariance construction for Gaussian priors preserves given marginals, permits arbitrary cross-correlations via contractions, and supports inference on the correlation structure itself.
Inverse problems: From regularization to Bayesian inference.Wiley Interdisciplinary Reviews: Computational Statistics, 10(3):e1427
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Beyond Independence: on Jointly Normal Priors in Bayesian Inversion
A joint covariance construction for Gaussian priors preserves given marginals, permits arbitrary cross-correlations via contractions, and supports inference on the correlation structure itself.