BibTeX

@article{2211.09602v2,
Author        = {Julia Linhart and Alexandre Gramfort and Pedro L. C. Rodrigues},
Title         = {Validation Diagnostics for SBI algorithms based on Normalizing Flows},
Eprint        = {2211.09602v2},
ArchivePrefix = {arXiv},
PrimaryClass  = {stat.ML},
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.},
Year          = {2022},
Month         = {Nov},
Url           = {http://arxiv.org/abs/2211.09602v2},
File          = {2211.09602v2.pdf}
}