BibTeX

@article{2509.03853v2,
Author        = {Haoyu Jiang and Yuexi Wang and Yun Yang},
Title         = {Simulation-based Inference via Langevin Dynamics with Score Matching},
Eprint        = {2509.03853v2},
ArchivePrefix = {arXiv},
PrimaryClass  = {stat.ME},
Abstract      = {Simulation-based inference (SBI) enables Bayesian analysis when the
likelihood is intractable but model simulations are available. Recent advances
in statistics and machine learning, including Approximate Bayesian Computation
and deep generative models, have expanded the applicability of SBI, yet these
methods often face challenges in moderate to high-dimensional parameter spaces.
Motivated by the success of gradient-based Monte Carlo methods in Bayesian
sampling, we propose a novel SBI method that integrates score matching with
Langevin dynamics to explore complex posterior landscapes more efficiently in
such settings. Our approach introduces tailored score-matching procedures for
SBI, including a localization scheme that reduces simulation costs and an
architectural regularization that embeds the statistical structure of
log-likelihood scores to improve score-matching accuracy. We provide
theoretical analysis of the method and illustrate its practical benefits on
benchmark tasks and on more challenging problems in moderate to high
dimensions, where it performs favorably compared to existing approaches.},
Year          = {2025},
Month         = {Sep},
Url           = {http://arxiv.org/abs/2509.03853v2},
File          = {2509.03853v2.pdf}
}