A joint covariance construction for Gaussian priors preserves given marginals, permits arbitrary cross-correlations via contractions, and supports inference on the correlation structure itself.
Study on joint inversion algorithm of acoustic and electromagnetic data in biomedical imaging.IEEE Journal on Multiscale and Multiphysics Computational Techniques, 4:2–11
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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.