用扩散模型加速模拟推断，快速准确估计隐藏参数。
Diffusion Models in Simulation-Based Inference: A Tutorial Review
- 基于得分的框架学习参数与观测的联合或条件分布。
- 不同噪声调度和采样器影响推断效率与统计精度。
- 适合需要高效模拟推断的研究者，尤其在参数维度高时。
扩散模型最近成为模拟推断（SBI）中的强大学习工具，可从模拟数据和真实数据中快速准确地估计隐变量参数。其基于得分的表述方式灵活地学习参数与观测之间的条件或联合分布，为多种建模问题提供通用解决方案。本文综述了扩散模型在SBI中的最新进展，涵盖训练、推断与评估的设计选择。重点探讨了引导、得分组合、流匹配、一致性模型及联合建模等概念带来的机遇。同时分析了噪声调度、参数化方式与采样器对效率和统计准确性的影响。通过跨参数维度、模拟预算与模型类型的案例研究展示相关方法，并提出未来研究的开放性问题。
原文摘要 · Abstract (English)
Diffusion models have recently emerged as powerful learners for simulation-based inference (SBI), enabling fast and accurate estimation of latent parameters from simulated and real data. Their score-based formulation offers a flexible way to learn conditional or joint distributions over parameters and observations, thereby providing a versatile solution to various modeling problems. In this tutorial review, we synthesize recent developments on diffusion models for SBI, covering design choices for training, inference, and evaluation. We highlight opportunities created by various concepts such as guidance, score composition, flow matching, consistency models, and joint modeling. Furthermore, we discuss how efficiency and statistical accuracy are affected by noise schedules, parameterizations, and samplers. Finally, we illustrate these concepts with case studies across parameter dimensionalities, simulation budgets, and model types, and outline open questions for future research.
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