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.

翻译：摊销化模拟器推理为工程学或神经科学等计算领域的贝叶斯推断提供了强大框架，其日益利用扩散模型等现代生成方法，将观测数据映射至模型参数或未来预测。这些方法通过对模拟参数-数据对进行训练后，无需额外调用模拟器即可为新数据集生成后验或后验预测样本。然而，其适用性常受限于训练阶段生成模型参数时所用的先验分布。为突破此限制，我们提出PriorGuide——一种专为基于扩散的摊销化推理方法设计的技术。PriorGuide采用创新的引导近似方法，使训练后的扩散模型能在测试时灵活适应新先验分布，且无需耗费大量计算资源进行重新训练。这使得用户能够在训练后便捷地整合更新信息或专家知识，从而增强预训练推理模型的泛用性。