发表机构
North Carolina State University; University of Chicago（北卡罗来纳州立大学; 芝加哥大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
提出可能性径向传输方法，通过深度学习实现近似IM推断，高效采样并评估覆盖率和功效，用于假设比较与选择，并在卵巢衰老数据上验证。
AI 中文摘要
在可能性推断模型（IMs）下，看到数据后探测假设空间仍然有效，前提是显著性水平保持固定。代价是计算量，因为每个可信度是假设上可能性轮廓的上确界，而轮廓本身在每个查询的参数值处被近似。我们提出了一种可能性径向传输，它将参数的轮廓值隐藏在源点的半径中。当选择最大化壳内熵的传输时，对覆盖置信截断的参数进行采样就变成了截断半径的问题。我们提供了一种深度学习算法，在最大化每个壳内熵的同时强制执行轮廓深度条件。我们的摊销使得对学习近似的覆盖率和功效评估以及新数据集的预测检查变得实用。我们还使用采样器构建Bel-Pl谱，用于比较和选择满足指定Bel-Pl决策准则的可解释假设。在模拟中，学习的轮廓匹配或改进了对截断的椭球近似，而覆盖率和功效跟踪精确参考。最后，我们使用合成AMH记录探测关于卵巢衰老的假设，询问每位女性其中位AMH水平将在多少年内保持高于指定参考值。
英文摘要
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.
