Normalizing flow decomposition enables likelihood-free inference with nuisance parameters

A new arXiv paper proposes splitting a neural normalizing flow into components that expose a quantity close to a pivotal statistic when nuisance parameters are present. The approach requires only a sample generator from the target distribution rather than an explicit likelihood. This could simplify inference in settings where the likelihood is intractable but simulation is straightforward.