基于密度比估计的先验变化引导

中文摘要 AI 辅助

基于模拟的推理（Simulation-Based Inference, SBI）是科学领域中参数推理的重要框架，适用于似然函数难以处理的模拟器场景；尽管摊销生成模型可实现快速后验估计，但通常受限于训练时使用的特定先验，灵活性随先验知识演变而受限。为解决该先验依赖问题，PriorGuide作为推理时的引导方法被提出，但其公式难以处理，需依赖反向转移核的高斯近似和先验比的高斯混合模型拟合，二者均会引入系统偏差。受这些局限的启发，我们提出一种无偏的测试时引导框架，利用密度比估计（Density Ratio Estimation, DRE）学习得分引导项，有效将推理过程与训练时的先验解耦；此外，该框架对特定密度比估计器无依赖，是处理先验变化的通用灵活框架。多项任务的实验结果表明，在多数任务中，我们的方法在C2ST和MMD指标上与PriorGuide相当或更优，且在训练与目标先验重叠有限时仍保持鲁棒性；我们还将该方法应用于行星光变曲线数据的参数推理贝叶斯更新，同样展现出强有效性与鲁棒性。代码可在该https URL获取。

英文摘要

Simulation-Based Inference (SBI) serves as a vital framework for parameter inference in scientific fields where simulators involve intractable likelihoods, yet while amortized generative models offer rapid posterior estimation, they are often restricted by the specific priors used during training, thereby limiting their flexibility as prior knowledge evolves. To address this prior dependency, PriorGuide was introduced as an inference-time guidance method, but due to its intractable formulation, it relies on Gaussian approximations of the reverse transition kernel and Gaussian mixture model fitting for the prior ratio, both of which introduce systematic bias. Motivated by these limitations, we propose an unbiased test-time guidance framework that leverages Density Ratio Estimation (DRE) to learn a score guidance term, effectively decoupling the inference process from the prior training. Moreover, our framework remains agnostic to the specific density ratio estimators, making it a general and flexible framework for handling prior changes. Experimental results across multiple tasks demonstrate that our method matches or outperforms PriorGuide on C2ST and MMD in most tasks while maintaining robustness even under limited overlap between the training and target priors. Furthermore, we apply our method to Bayesian updating for parameter inference from planetary light-curve data, where it also demonstrates strong effectiveness and robustness. Code is available at https://github.com/a-chenchen/dre-based-prior-guidance .

发表机构

• University of Bristol（布里斯托尔大学）

机构由 AI 辅助整理，请以论文原文为准。