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Research questionHow can iterative data-consistent inversion recover joint dependence in generalized stochastic inverse problems?Sequential inversion can satisfy individual observed push-forward distributions without reproducing the dependence structure of the full observed joint distribution. The resulting gap between iterative and joint solutions depends on how their joint distributions differ.
Statistical Machine Learning
Latest papersRecent research connected to this question, newest first.Copula Transformations for Data-Consistent InversionThe source studies iterative data-consistent inversion for generalized stochastic inverse problems. Copula theory characterizes the residual discrepancy, while theoretical results cover exact and approximate copula transformations, converging reference measures, and progressively enriched feasible sets; numerical examples examine quantity-of-interest geometry, adaptive reference-measure refinement under a fixed sampling budget, and heterogeneous asynchronously acquired experiments.research paper · Sep 6, 2026
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