Preconditioned Robust Neural Posterior Estimation for Misspecified Simulators

• URL: http://arxiv.org/abs/2602.18004v1
• Date: Fri, 20 Feb 2026 05:32:35 GMT
• Title: Preconditioned Robust Neural Posterior Estimation for Misspecified Simulators
• Authors: Ryan P. Kelly, David T. Frazier, David J. Warne, Christopher C. Drovandi,
• Abstract summary: We study preconditioning under misspecification and propose preconditioned robust neural posterior estimation.<n>We demonstrate that preconditioning combined with robust NPE increases stability and improves accuracy, calibration, and posterior-predictive fit over standard baseline methods.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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