Spectra：基于模拟推断中测试时先验适应的精确分量传输
Spectra: Exact Component Transport for Test-Time Prior Adaptation in Simulation-Based Inference
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中文总结 AI 辅助
本文提出Spectra，一种用于基于扩散的模拟推断的测试时先验适应方法，通过精确分数传输恒等式实现闭式适应，无需额外模拟或训练，在六个基准上实现强先验偏移下的准确适应，使预训练模型能利用更新先验。
中文摘要 AI 辅助
基于模拟的推断（SBI）已成为在似然函数难以或无法评估的复杂科学模型中进行贝叶斯推断的强大方法。摊销式SBI从模拟数据中学习可重用的推断模型，从而能够对新观测进行快速后验推断，而现代生成模型已使这些模型越来越具有表达力。然而，这种重用仅限于训练期间选择的先验分布，而科学分析通常需要随着知识积累或测试替代假设而修订先验。我们引入了Spectra，一种用于基于扩散的SBI的测试时适应方法。Spectra利用精确的分数传输恒等式，以闭式形式从冻结的扩散模型中获得适应后的分数，适用于结构化先验变化，无需额外模拟或训练。在六个SBI基准测试中，Spectra在强先验偏移下实现了准确的适应，且在线采样成本较低。这使得预训练的SBI模型能够在测试时纳入更新的先验信息。
英文摘要
Simulation-based inference (SBI) has become a powerful approach to Bayesian inference in complex scientific models whose likelihoods are difficult or impossible to evaluate. Amortized SBI learns reusable inference models from simulated data, enabling rapid posterior inference for new observations, and modern generative models have made these models increasingly expressive. However, this reuse is limited to the prior distribution chosen during training, whereas scientific analyses often need revised priors as knowledge accumulates or alternative assumptions are tested. We introduce Spectra, a test-time adaptation method for diffusion-based SBI. Spectra uses an exact score-transport identity to obtain the adapted score from a frozen diffusion model in closed form for structured prior changes, without additional simulation or training. Across six SBI benchmarks, Spectra achieves accurate adaptation under strong prior shifts at low online sampling cost. This enables pretrained SBI models to incorporate updated prior information at test time.
发表机构
- Max Planck Institute for Human Cognitive and Brain Sciences（马克斯·普朗克人类认知与脑科学研究所）
- International Max Planck Research School on Cognitive NeuroImaging（马克斯·普朗克国际认知神经影像研究学院）
- Center for Scalable Data Analytics and Artificial Intelligence (ScaDS.AI)（可扩展数据分析与人工智能中心（ScaDS.AI））
- Leipzig University（莱比锡大学）
- University College London（伦敦大学学院）
机构由 AI 辅助整理，请以论文原文为准。
