BibTeX

@article{2602.18004v1,
Author        = {Ryan P. Kelly and David T. Frazier and David J. Warne and Christopher C. Drovandi},
Title         = {Preconditioned Robust Neural Posterior Estimation for Misspecified Simulators},
Eprint        = {2602.18004v1},
ArchivePrefix = {arXiv},
PrimaryClass  = {stat.ME},
Abstract      = {Simulation-based inference (SBI) enables parameter estimation for complex stochastic models with intractable likelihoods when model simulation is feasible. Neural posterior estimation (NPE) is a popular SBI approach that often achieves accurate inference with far fewer simulations than classical approaches. But in practice, neural approaches can be unreliable for two reasons: incompatible data summaries arising from model misspecification yield unreliable posteriors due to extrapolation, and prior-predictive draws can produce extreme summaries that lead to difficulties in obtaining an accurate posterior for the observed data of interest. Existing preconditioning schemes target well-specified settings, and their behaviour under misspecification remains unexplored. We study preconditioning under misspecification and propose preconditioned robust neural posterior estimation, which computes data-dependent weights that focus training near the observed summaries and fits a robust neural posterior approximation. We also introduce a forest-proximity preconditioning approach that uses tree-based proximity scores to down-weight outlying simulations and concentrate computation around the observed dataset. Across two synthetic examples and one real example with incompatible summaries and extreme prior-predictive behaviour, we demonstrate that preconditioning combined with robust NPE increases stability and improves accuracy, calibration, and posterior-predictive fit over standard baseline methods.},
Year          = {2026},
Month         = {Feb},
Url           = {http://arxiv.org/abs/2602.18004v1},
File          = {2602.18004v1.pdf}
}