让生成模型在测试时灵活适配新先验，无需重新训练。
PriorGuide: Test-Time Prior Adaptation for Simulation-Based Inference
- 用新方法在测试阶段动态调整生成模型的先验分布。
- 无需重训练即可适配新先验，保持高效推理能力。
- 适合需要快速更新知识的工程与神经科学场景。
基于模拟器的近似推断为工程和神经科学等计算领域中的贝叶斯推断提供了强大框架，越来越多地采用扩散模型等生成方法，将观测数据映射到模型参数或未来预测。这些方法在对模拟参数-数据对进行训练后，可对新数据集生成后验或后验预测样本，而无需再次调用模拟器。然而，其应用常受限于训练阶段使用的先验分布。为此，我们提出 PriorGuide，一种专为基于扩散的近似推断方法设计的技术。PriorGuide 利用一种新颖的引导近似，实现训练后扩散模型在测试时对新先验的灵活适应，关键在于无需高昂的重训练成本。这使得用户可在训练后轻松融入更新信息或专家知识，提升预训练推断模型的灵活性与实用性。
原文摘要 · Abstract (English)
Amortized simulator-based inference offers a powerful framework for tackling Bayesian inference in computational fields such as engineering or neuroscience, increasingly leveraging modern generative methods like diffusion models to map observed data to model parameters or future predictions. These approaches yield posterior or posterior-predictive samples for new datasets without requiring further simulator calls after training on simulated parameter-data pairs. However, their applicability is often limited by the prior distribution(s) used to generate model parameters during this training phase. To overcome this constraint, we introduce PriorGuide, a technique specifically designed for diffusion-based amortized inference methods. PriorGuide leverages a novel guidance approximation that enables flexible adaptation of the trained diffusion model to new priors at test time, crucially without costly retraining. This allows users to readily incorporate updated information or expert knowledge post-training, enhancing the versatility of pre-trained inference models.
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