Probabilistic graphical models (PGMs) are widely used to discover latent structure in data, but their success hinges on selecting an appropriate model design. In practice, model specification is difficult and often requires iterative trial-and-error. This challenge arises because classical PGMs typically operate on individual datasets. In this work, we consider settings involving collections of related datasets and propose meta-probabilistic modeling (MPM) to learn the generative model structure itself. MPM uses a hierarchical formulation in which global components encode shared patterns across datasets, while local parameters capture dataset-specific latent structure. For scalable learning and inference, we derive a tractable VAE-inspired surrogate objective together with a bi-level optimization algorithm. Our methodology supports a broad class of expressive probabilistic models and has connections to existing architectures, such as Slot Attention. Experiments on object-centric representation learning and sequential text modeling demonstrate that MPM effectively adapts generative models to data while recovering meaningful latent representations.

翻译：概率图模型（PGMs）被广泛用于发现数据中的潜在结构，但其成功取决于选择恰当的模型设计。在实践中，模型规范制定困难且往往需要反复试错。这一挑战源于经典PGM通常作用于单个数据集。本文考虑涉及相关数据集合的场景，提出元概率建模（MPM）来学习生成模型结构本身。MPM采用层次化框架，其中全局组件编码跨数据集的共享模式，而局部参数捕获数据集特定的潜在结构。为实现可扩展的学习与推理，我们推导出受VAE启发的可处理替代目标函数，并结合双层优化算法。该方法支持广泛的表达性概率模型并与现有架构（如Slot Attention）存在关联。在面向对象的表示学习与序列文本建模实验表明，MPM能有效使生成模型适应数据，同时恢复有意义的潜在表征。