arXiv：cs.LG· Jungeum Kim, Percy Zhai·· 4 小时前AI 评分27
面向近似 IM 推理的可能性径向传输
Possibilistic Radial Transport for Approximate IM Inference
AI 导读
研究者提出“可能性径向传输”方法，将参数的可能性轮廓值隐藏在其源点半径中，当选取壳内熵最大化的传输时，只需截断半径即可采样覆盖置信切片的参数。配套深度学习算法在满足轮廓深度条件下最大化各壳内熵，使学习近似值的覆盖率和功效评估及新数据集预测检查变得可行。模拟中学习到的轮廓匹配或优于椭球近似，覆盖率和功效与精确参考一致，并用于合成 AMH 数据探究卵巢衰老假设。
正文
Abstract:Probing the hypothesis space after seeing the data remains valid under possibilistic inferential models (IMs), provided the significance level stays fixed. The price is computation, as each plausibility is a supremum of the possibility contour over the hypothesis, and the contour itself is approximated at each queried parameter value. We propose a possibilistic radial transport, which hides the contour value of a parameter in the radius of its source point. When a transport that maximizes within-shell entropy is picked, sampling parameters covering a confidence cut becomes a matter of truncating the radius. We provide a deep learning algorithm that enforces the contour depth condition while maximizing the entropy within each shell. Our amortization makes coverage and power assessments of the learned approximation practical as well as predictive check of new datasets. We also use the sampler to construct a Bel-Pl spectrum for comparing and selecting interpretable hypotheses that satisfy a prescribed Bel-Pl decision criterion. In simulations the learned contours match or improve on ellipsoidal approximations to the cuts, while the coverage and power track the exact reference. Finally, we probe hypotheses about ovarian aging using synthetic AMH records, asking for each woman how many more years her median AMH level will remain above a specified reference value.
| Subjects: | Machine Learning (stat.ML); Machine Learning (cs.LG) |
| Cite as: | arXiv:2610.09956 [stat.ML] |
| (or arXiv:2610.09956v1 [stat.ML] for this version) | |
| https://doi.org/10.48550/arXiv.2610.09956 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Jungeum Kim [view email]
[v1]
Wed, 7 Oct 2026 12:32:04 UTC (4,184 KB)
来源：arXiv：cs.LG · arxiv.org