BibTeX

@article{2308.01054v3,
Author        = {Simon Dirmeier and Carlo Albert and Fernando Perez-Cruz},
Title         = {Simulation-based Inference for High-dimensional Data using Surjective
  Sequential Neural Likelihood Estimation},
Eprint        = {2308.01054v3},
ArchivePrefix = {arXiv},
PrimaryClass  = {stat.ML},
Abstract      = {Neural likelihood estimation methods for simulation-based inference can
suffer from performance degradation when the modeled data is very
high-dimensional or lies along a lower-dimensional manifold, which is due to
the inability of the density estimator to accurately estimate a density
function. We present Surjective Sequential Neural Likelihood (SSNL) estimation,
a novel member in the family of methods for simulation-based inference (SBI).
SSNL fits a dimensionality-reducing surjective normalizing flow model and uses
it as a surrogate likelihood function, which allows for computational inference
via Markov chain Monte Carlo or variational Bayes methods. Among other
benefits, SSNL avoids the requirement to manually craft summary statistics for
inference of high-dimensional data sets, since the lower-dimensional
representation is computed simultaneously with learning the likelihood and
without additional computational overhead. We evaluate SSNL on a wide variety
of experiments, including two challenging real-world examples from the
astrophysics and neuroscience literatures, and show that it either outperforms
or is on par with state-of-the-art methods, making it an excellent
off-the-shelf estimator for SBI for high-dimensional data sets.},
Year          = {2023},
Month         = {Aug},
Url           = {http://arxiv.org/abs/2308.01054v3},
File          = {2308.01054v3.pdf}
}