发表机构
TU Dortmund University; University of Stuttgart; Rensselaer Polytechnic Institute（多特蒙德工业大学; 斯图加特大学; 伦斯勒理工学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
提出一种通用方法，通过图展开与反转自动推导多层模型后验的因子分解和网络架构，保留全部条件独立性，在6500+参数模型上媲美采样器且推断近乎即时。
AI 中文摘要
我们开发了一种针对任意结构多层模型的摊销贝叶斯推断的通用方法。给定一个以有向无环图形式指定的生成模型，我们的方法自动推导出联合后验的有效分解以及匹配的神经网络架构。关键步骤——图展开和图反转——产生一个逆图，该图决定了推断网络如何堆叠和条件化，从而产生在组数量和每组内观测数量上摊销的分解。与为加速学习或推断而简化依赖结构的方法不同，我们的方法保留了生成模型的所有条件独立性和可交换性假设。在三个案例研究中，该方法在参数超过6500个的模型上与黄金标准采样器紧密匹配，同时将推断缩减为训练后近乎即时的前向传播。
英文摘要
We develop a general method for amortized Bayesian inference on multilevel models of arbitrary structure. Given a generative model specified as a directed acyclic graph, our method automatically derives valid factorizations of the joint posterior and matching neural network architectures. The key steps, graph expansion and graph inversion, yield an inverse graph that determines how inference networks are stacked and conditioned, producing factorizations that amortize over the number of groups and the number of observations within each group. Unlike approaches that simplify the dependency structure to speed up learning or inference, our method preserves all conditional independence and exchangeability assumptions of the generative model. Across three case studies, it closely matches gold-standard samplers on models with more than 6,500 parameters while reducing inference to a near-instant forward pass once trained.
Comments16 pages, 3 figures
