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.

翻译：扩散模型最近已成为基于模拟的推断（SBI）中强大的学习器，能够从模拟和真实数据中快速准确地估计潜在参数。其基于分数的公式为学习参数与观测值的条件或联合分布提供了灵活的方法，从而为各种建模问题提供了通用解决方案。在本教程综述中，我们综合了扩散模型在SBI领域的最新进展，涵盖训练、推断和评估的设计选择。我们重点阐述了引导、分数组合、流匹配、一致性模型和联合建模等多种概念带来的机遇。此外，我们讨论了噪声调度、参数化和采样器如何影响效率与统计准确性。最后，我们通过跨参数维度、模拟预算和模型类型的案例研究阐释了这些概念，并展望了未来研究的开放性问题。