BibTeX

@article{2308.08597v2,
Author        = {Noemi Anau Montel and James Alvey and Christoph Weniger},
Title         = {Scalable inference with Autoregressive Neural Ratio Estimation},
Eprint        = {2308.08597v2},
DOI           = {10.1093/mnras/stae1130},
ArchivePrefix = {arXiv},
PrimaryClass  = {astro-ph.IM},
Abstract      = {In recent years, there has been a remarkable development of simulation-based
inference (SBI) algorithms, and they have now been applied across a wide range
of astrophysical and cosmological analyses. There are a number of key
advantages to these methods, centred around the ability to perform scalable
statistical inference without an explicit likelihood. In this work, we propose
two technical building blocks to a specific sequential SBI algorithm, truncated
marginal neural ratio estimation (TMNRE). In particular, first we develop
autoregressive ratio estimation with the aim to robustly estimate correlated
high-dimensional posteriors. Secondly, we propose a slice-based nested sampling
algorithm to efficiently draw both posterior samples and constrained prior
samples from ratio estimators, the latter being instrumental for sequential
inference. To validate our implementation, we carry out inference tasks on
three concrete examples: a toy model of a multi-dimensional Gaussian, the
analysis of a stellar stream mock observation, and finally, a proof-of-concept
application to substructure searches in strong gravitational lensing. In
addition, we publicly release the code for both the autoregressive ratio
estimator and the slice sampler.},
Year          = {2023},
Month         = {Aug},
Note          = {Monthly Notices of the Royal Astronomical Society, Volume 530,
  Issue 4, June 2024, Pages 4107-4124},
Url           = {http://arxiv.org/abs/2308.08597v2},
File          = {2308.08597v2.pdf}
}