用预训练表格模型做贝叶斯推断的摘要网络，无需训练即可适配多种任务。
Pre-trained Tabular Foundation Models as Versatile Summary Networks for Neural Posterior Estimation
- 用预训练的TabPFN提取模拟数据摘要，通过上下文学习实时适配。
- 在多个任务中表现接近甚至优于传统方法，且能保留后验位置和边际信息。
- 适合需要快速部署、模块化推理头的贝叶斯推断场景。
本文研究将TabPFN作为无训练、模块化的摘要网络用于基于模拟的贝叶斯推断（SBI）。TabPFN等表格基础模型在广泛合成表格数据生成过程中预训练，并通过上下文学习在测试时自适应，天然适用于依赖有效模拟观测摘要的后验估计。我们提出PFN-NPE：一种通用框架，使用预训练的TabPFN编码器作为固定摘要网络处理模拟器输出，再与针对具体问题选择的下游推断头结合。默认采用归一化流作为推断头，该方法在后验逼近上达到现有水平，有时更优。诊断分析表明，由TabPFN生成的摘要通常保留了有用的后验位置和边际信息。但同时也发现，即使边际信息良好恢复，其对联合后验结构的表达仍存在局限。实验显示，TabPFN可在多样化的SBI设置中作为有效摘要网络，推断网络保持模块化且任务相关。
原文摘要 · Abstract (English)
In this work, we study TabPFN as a training-free, modular summary network for simulation-based Bayesian inference (SBI). Tabular foundation models such as TabPFN are pretrained on broad families of synthetic tabular data-generating processes and adapt at test time through in-context learning, making them natural candidates for SBI, where posterior estimation often depends on learning informative summaries of simulated observations. We propose PFN-NPE: a general recipe that uses a pretrained TabPFN encoder as a fixed summary network for simulator outputs, then pairs the resulting summaries with a downstream inference head chosen for the problem. With normalizing flows as the default inference head, PFN-NPE matches established posterior approximation methods and sometimes outperforms them. More importantly, diagnostic probes show that the TabPFN-derived summaries often preserve useful posterior location and marginal information. These analyses also reveal a limitation in that TabPFN-derived summaries may struggle to represent the joint posterior structure even when the marginals are well recovered. Still, our experiments show that TabPFN can serve as an effective summary network across a diverse set of SBI settings, with the inference network left modular and task-dependent.
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