BibTeX

@article{2509.05724v1,
Author        = {Matthew O'Callaghan and Kaisey S. Mandel and Gerry Gilmore},
Title         = {Robust variational neural posterior estimation for simulation-based
  inference},
Eprint        = {2509.05724v1},
ArchivePrefix = {arXiv},
PrimaryClass  = {stat.ML},
Abstract      = {Recent advances in neural density estimation have enabled powerful
simulation-based inference (SBI) methods that can flexibly approximate Bayesian
inference for intractable stochastic models. Although these methods have
demonstrated reliable posterior estimation when the simulator accurately
represents the underlying data generative process (GDP), recent work has shown
that they perform poorly in the presence of model misspecification. This poses
a significant problem for their use on real-world problems, due to simulators
always misrepresenting the true DGP to a certain degree. In this paper, we
introduce robust variational neural posterior estimation (RVNP), a method which
addresses the problem of misspecification in amortised SBI by bridging the
simulation-to-reality gap using variational inference and error modelling. We
test RVNP on multiple benchmark tasks, including using real data from
astronomy, and show that it can recover robust posterior inference in a
data-driven manner without adopting tunable hyperparameters or priors governing
the misspecification.},
Year          = {2025},
Month         = {Sep},
Url           = {http://arxiv.org/abs/2509.05724v1},
File          = {2509.05724v1.pdf}
}