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 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：本文对易处理概率生成建模领域的进展与技术进行了全面综述，主要聚焦于概率电路（PCs）。我们为表达能力与易处理性之间的内在权衡提供了统一视角，重点阐述了构建高表达性且高效PCs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概念来构建深度与混合PCs的最新尝试，并概述了能够指导这一不断演进领域未来研究的挑战与开放性问题。