提出一种可复用且能严格抵御异常值的模拟推断方法。
Amortised and provably-robust simulation-based inference
- 基于广义贝叶斯与神经加权得分匹配，实现可复用推断。
- 在存在极端数据时仍保持稳定，计算开销仅为现有方法一小部分。
- 无需马尔可夫链蒙特卡洛采样，适合高效率场景。
复杂模拟器模型现被广泛用于科学与工程中的推断任务，但现有方法常无法处理因仪器故障或人为错误导致的数据异常值。本文提出一种基于广义贝叶斯推断与神经加权得分匹配损失近似的新型模拟推断方法，兼具可复用性与对异常值的严格鲁棒性，这是现有方法尚未实现的组合。通过精心设计的条件密度模型，我们进一步简化了推断过程，无需马尔可夫链蒙特卡洛（MCMC）采样，显著降低计算成本，复杂度仅为当前最先进方法的一小部分。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。