BibTeX

@article{2504.11925v1,
Author        = {David Refaeli and Mira Marcus-Kalish and David M. Steinberg},
Title         = {Reducing Calls to the Simulator in Simulation Based Inference (SBI)},
Eprint        = {2504.11925v1},
ArchivePrefix = {arXiv},
PrimaryClass  = {stat.CO},
Abstract      = {Simulation-Based Inference (SBI) deals with statistical inference in problems
where the data are generated from a system that is described by a complex
stochastic simulator. The challenge for inference in these problems is that the
likelihood is intractable; SBI proceeds by using the simulator to sample from
the likelihood. In many real world applications, simulator calls are expensive,
limiting the associated sample size. Our goal in this work is to extend SBI to
exploit two proposals for reducing simulator calls: to draw likelihood samples
from a Neural Density Estimator (NDE) surrogate rather than from the stochastic
simulator; and use of Support Points rather than simple random sampling to
generate evaluation sites. We embed these methods in the Sequential Neural
Posterior Estimator (SNPE) algorithm. Across a suite of test cases, we find
that the NDE surrogate improves the quality of the inference; support points
worked well in some examples, but not in others.},
Year          = {2025},
Month         = {Apr},
Url           = {http://arxiv.org/abs/2504.11925v1},
File          = {2504.11925v1.pdf}
}