Complex simulator-based models are now routinely used to perform inference across the sciences and engineering, but existing inference methods are often unable to account for outliers and other extreme values in data which occur due to faulty measurement instruments or human error. In this paper, we introduce a novel approach to simulation-based inference grounded in generalised Bayesian inference and a neural approximation of a weighted score-matching loss. This leads to a method that is both amortised and provably robust to outliers, a combination not achieved by existing approaches. Furthermore, through a carefully chosen conditional density model, we demonstrate that inference can be further simplified and performed without the need for Markov chain Monte Carlo sampling, thereby offering significant computational advantages, with complexity that is only a small fraction of that of current state-of-the-art approaches.

翻译：复杂仿真模型现已在科学与工程领域被常规用于执行推断，但现有推断方法通常无法处理因测量仪器故障或人为错误导致的数据异常值及其他极端值。本文提出一种基于广义贝叶斯推断与加权评分匹配损失的神经网络近似的新型仿真推断方法。该方法兼具摊销特性与对异常值的可证明鲁棒性，这是现有方法未能实现的组合。此外，通过精心设计的条件密度模型，我们证明推断过程可进一步简化，无需依赖马尔可夫链蒙特卡洛采样，从而提供显著的计算优势，其复杂度仅为当前最先进方法的极小部分。