BibTeX

@article{2506.06087v3,
Author        = {Yuga Hikida and Ayush Bharti and Niall Jeffrey and François-Xavier Briol},
Title         = {Multilevel neural simulation-based inference},
Eprint        = {2506.06087v3},
ArchivePrefix = {arXiv},
PrimaryClass  = {stat.ML},
Abstract      = {Neural simulation-based inference (SBI) is a popular set of methods for
Bayesian inference when models are only available in the form of a simulator.
These methods are widely used in the sciences and engineering, where writing
down a likelihood can be significantly more challenging than constructing a
simulator. However, the performance of neural SBI can suffer when simulators
are computationally expensive, thereby limiting the number of simulations that
can be performed. In this paper, we propose a novel approach to neural SBI
which leverages multilevel Monte Carlo techniques for settings where several
simulators of varying cost and fidelity are available. We demonstrate through
both theoretical analysis and extensive experiments that our method can
significantly enhance the accuracy of SBI methods given a fixed computational
budget.},
Year          = {2025},
Month         = {Jun},
Url           = {http://arxiv.org/abs/2506.06087v3},
File          = {2506.06087v3.pdf}
}