Validation Diagnostics for SBI algorithms based on Normalizing Flows

TL;DR

This paper introduces interpretable validation diagnostics for Normalizing Flows in simulation-based inference, providing theoretical guarantees and practical tools to assess the consistency of high-dimensional posterior estimators.

Contribution

It develops new validation methods for multi-dimensional NF-based SBI algorithms with theoretical guarantees, addressing a gap in existing metrics.

Findings

Validation diagnostics effectively assess NF estimators in complex scenarios.

The method provides theoretical guarantees of local consistency.

Application to neuroscience data demonstrates practical utility.

Abstract

Building on the recent trend of new deep generative models known as Normalizing Flows (NF), simulation-based inference (SBI) algorithms can now efficiently accommodate arbitrary complex and high-dimensional data distributions. The development of appropriate validation methods however has fallen behind. Indeed, most of the existing metrics either require access to the true posterior distribution, or fail to provide theoretical guarantees on the consistency of the inferred approximation beyond the one-dimensional setting. This work proposes easy to interpret validation diagnostics for multi-dimensional conditional (posterior) density estimators based on NF. It also offers theoretical guarantees based on results of local consistency. The proposed workflow can be used to check, analyse and guarantee consistent behavior of the estimator. The method is illustrated with a challenging example that involves tightly coupled parameters in the context of computational neuroscience. This work should help the design of better specified models or drive the development of novel SBI-algorithms, hence allowing to build up trust on their ability to address important questions in experimental science.