Fabian Schneider
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2026
A Convex Approximation Framework for Neural Likelihood-Based Bayesian Inverse Problems
This paper improves the foundations of neural likelihood approximation for Bayesian inverse problems by making the learning problem strictly convex and showing convergence to the true likelihood.
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