A joint covariance construction for Gaussian priors preserves given marginals, permits arbitrary cross-correlations via contractions, and supports inference on the correlation structure itself.
An inexact gauss-newton method for inversion of basal sliding and rheology parameters in a nonlinear stokes ice sheet model.Journal of Glaciology, 58(211):889–903
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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.