预条件鲁棒神经后验估计用于规格不明确的模拟器
Preconditioned Robust Neural Posterior Estimation for Misspecified Simulators
AI总结：
本文提出预条件鲁棒神经后验估计方法，通过数据依赖权重和森林邻近预条件化提升模型在规格不明确下的稳定性与准确性。
摘要：
基于模拟的推断（SBI）在模型模拟可行时，能够对具有不可 tractable 似然的复杂随机模型进行参数估计。神经后验估计（NPE）是一种流行的 SBI 方法，通常能够以远少于经典方法的模拟次数实现准确的推断。但实践中，神经方法不可靠有两个原因：由于模型规格不明确导致的数据汇总不兼容，从而因外推产生不可靠的后验；以及先验预测抽样可以产生极端汇总，导致难以获得感兴趣的观测数据的准确后验。现有的预条件化方案针对 well-specified 的情况，其在规格不明确下的行为尚无研究。我们研究了在规格不明确下的预条件化，并提出了预条件化鲁棒神经后验估计，该方法计算数据依赖的权重，使训练集中在观测汇总附近，并拟合一个鲁棒的神经后验近似。我们还引入了一种森林邻近预条件化方法，利用基于树的邻近分数来降低离群模拟的权重，并将计算集中在观测数据集上。在两个合成示例和一个具有不兼容汇总和极端先验预测行为的真实示例中，我们证明了预条件化结合鲁棒 NPE 能提高稳定性，改善准确性、校准和后验预测拟合，优于标准基线方法。
英文摘要：
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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