arXiv：cs.LG· Simon Dirmeier, Antonietta Mira·· 4 小时前AI 评分33
Causal Posterior Estimation：将条件依赖结构硬编码进网络的贝叶斯推理新方法
Causal Posterior Estimation
AI 导读
研究者提出 Causal Posterior Estimation（CPE），一种面向模拟器模型的贝叶斯推理方法，适用于似然函数难以计算但按参数生成输出容易的场景。CPE 用 flow matching 近似后验分布，并将模型图结构诱导的条件依赖关系直接写入神经网络架构，而非让网络从数据中学习。实验显示，这种硬编码方式使 CPE 的后验推理精度达到或超过当前最优基线。
正文
Abstract:We present Causal Posterior Estimation (CPE), a novel method for Bayesian inference in simulator models, where evaluating the likelihood function is intractable or computationally expensive, but generating outputs given parameter values is straightforward. CPE approximates the posterior distribution using flow matching while directly incorporating the conditional dependence structure induced by the model's graphical representation into the neural network architecture. Across extensive experiments, we demonstrate that hard-coding these conditional dependencies into the network, rather than requiring them to be learned from data, enables CPE to achieve highly accurate posterior inference that matches or outperforms state-of-the-art baselines.
| Subjects: | Machine Learning (cs.LG); Machine Learning (stat.ML) |
| Cite as: | arXiv:2505.21468 [cs.LG] |
| (or arXiv:2505.21468v2 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2505.21468 arXiv-issued DOI via DataCite |
Submission history
From: Simon Dirmeier [view email]
[v1]
Tue, 27 May 2025 17:41:21 UTC (594 KB)
[v2]
Wed, 7 Oct 2026 17:41:22 UTC (6,227 KB)
来源：arXiv：cs.LG · arxiv.org