A joint covariance construction for Gaussian priors preserves given marginals, permits arbitrary cross-correlations via contractions, and supports inference on the correlation structure itself.
Randomize-then-optimize: A method for sampling from posterior distributions in nonlinear inverse problems.SIAM Journal on Scientific Com- puting, 36(4):A1895–A1910
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
stat.ME 1years
2026 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
-
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.