We present a comprehensive survey of the advancements and techniques in the field of tractable probabilistic generative modeling, primarily focusing on Probabilistic Circuits (PCs). We provide a unified perspective on the inherent trade-offs between expressivity and the tractability, highlighting the design principles and algorithmic extensions that have enabled building expressive and efficient PCs, and provide a taxonomy of the field. We also discuss recent efforts to build deep and hybrid PCs by fusing notions from deep neural models, and outline the challenges and open questions that can guide future research in this evolving field.

翻译：我们全面综述了易于处理的概率生成建模领域的进展与技术，重点关注概率电路（PCs）。本文提供了关于表达力与可处理性之间固有权衡的统一视角，阐述了实现构建富有表达力且高效的PC的设计原则与算法扩展，并给出了该领域的分类体系。我们还讨论了通过融合深度神经模型的概念来构建深度与混合型PC的最新努力，并概述了可指导这一发展领域未来研究的挑战与开放性问题。