A joint covariance construction for Gaussian priors preserves given marginals, permits arbitrary cross-correlations via contractions, and supports inference on the correlation structure itself.
Model fusion and joint inversion.Surveys in Geophysics, 34(5):675– 695
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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.