An iterative data-consistent inversion procedure converges to a measure satisfying multiple push-forward constraints, minimizing cumulative f-divergence and yielding the maximum-entropy solution under uniform initialization.
and O’Hagan, A., Bayesian calibration of computer models,Journal of the Royal Sta- tistical Society: Series B (Statistical Methodology), 63(3):425–464
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Iterative Data-Consistent Inversion with Multiple Push-forward Constraints
An iterative data-consistent inversion procedure converges to a measure satisfying multiple push-forward constraints, minimizing cumulative f-divergence and yielding the maximum-entropy solution under uniform initialization.