Building Expressive and Tractable Probabilistic Generative Models: A Review

[Submitted on 1 Feb 2024 (v1), last revised 31 Aug 2026 (this version, v4)]

View PDF HTML (experimental)

Abstract: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.

Submission history

From: Sahil Sidheekh [view email]
[v1] Thu, 1 Feb 2024 16:49:27 UTC (210 KB)
[v2] Wed, 22 May 2024 03:44:21 UTC (50 KB)
[v3] Thu, 6 Jun 2024 11:25:34 UTC (214 KB)
[v4] Mon, 31 Aug 2026 22:34:47 UTC (46 KB)