BibTeX

@article{2510.15817v1,
Author        = {Camille Touron and Gabriel V. Cardoso and Julyan Arbel and Pedro L. C. Rodrigues},
Title         = {Error analysis of a compositional score-based algorithm for
  simulation-based inference},
Eprint        = {2510.15817v1},
ArchivePrefix = {arXiv},
PrimaryClass  = {stat.ML},
Abstract      = {Simulation-based inference (SBI) has become a widely used framework in
applied sciences for estimating the parameters of stochastic models that best
explain experimental observations. A central question in this setting is how to
effectively combine multiple observations in order to improve parameter
inference and obtain sharper posterior distributions. Recent advances in
score-based diffusion methods address this problem by constructing a
compositional score, obtained by aggregating individual posterior scores within
the diffusion process. While it is natural to suspect that the accumulation of
individual errors may significantly degrade sampling quality as the number of
observations grows, this important theoretical issue has so far remained
unexplored. In this paper, we study the compositional score produced by the
GAUSS algorithm of Linhart et al. (2024) and establish an upper bound on its
mean squared error in terms of both the individual score errors and the number
of observations. We illustrate our theoretical findings on a Gaussian example,
where all analytical expressions can be derived in a closed form.},
Year          = {2025},
Month         = {Oct},
Url           = {http://arxiv.org/abs/2510.15817v1},
File          = {2510.15817v1.pdf}
}