SOURCE-LINKED INTELLIGENCE

Likelihood-free inference with nuisance parameters through normalizing flows

arXiv · AI, language, vision and robotics · article · Sep 9, 2026 · UTC

We present a simple decomposition of a neural-network-based normalizing flow that naturally uncovers a pivotal statistic (or something close) in the presence of nuisance parameters, based only on a sample generator from the distribution of interest. We show that the statistic is near-pivotal in the sense of minimum average KL-divergence of its $p$-values versus uniform and we argue that it can be expected to have good power when the dimension of the statistic equals the dimension of the parameter. It is able to incorporate prior knowledge about group invariances such as translation and scale.

Read original source ↗ Open in workspace

recordType

paper

region

Global