Bayesian structure learning allows inferring Bayesian network structure from data while reasoning about the epistemic uncertainty -- a key element towards enabling active causal discovery and designing interventions in real world systems. In this work, we propose a general, fully differentiable framework for Bayesian structure learning (DiBS) that operates in the continuous space of a latent probabilistic graph representation. Building on recent advances in variational inference, we use DiBS to devise an efficient method for approximating posteriors over structural models. Contrary to existing work, DiBS is agnostic to the form of the local conditional distributions and allows for joint posterior inference of both the graph structure and the conditional distribution parameters. This makes our method directly applicable to posterior inference of nonstandard Bayesian network models, e.g., with nonlinear dependencies encoded by neural networks. In evaluations on simulated and real-world data, DiBS significantly outperforms related approaches to joint posterior inference.

本文提出了一个完全可微分的贝叶斯结构学习框架（DiBS），它操作在潜在概率图表达的连续空间中，并且对于局部条件分布的形式不加区分，允许联合后验推断图形结构和条件分布参数，从而直接适用于后验推断复杂的贝叶斯网络模型。使用DiBS，我们设计了一种有效的通用变分推理方法，以近似结构模型的分布。实验结果表明，我们的方法在联合后验推断方面显著优于相关方法。

DiBS: 可微分的贝叶斯结构学习