arxivstat.MLcs.LGmath.PRstat.ME2026-07-07
A Convex Approximation Framework for Neural Likelihood-Based Bayesian Inverse Problems
Fabian Schneider, Tapio Helin, Leila Taghizadeh
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 classical…