arXiv stat.ML
· Papers
A Convex Approximation Framework for Neural Likelihood-Based Bayesian Inverse Problems
arXiv:2607.06252v1 Announce Type: new Abstract: Many problems in science and engineering are difficult to model accurately, either due to unknown physical mechanisms, poorly quantified measurement uncertainty, or prohibitive computational costs of high-fidelity simulations. These challenges limit the applicability of c