Building Expressive and Tractable Probabilistic Generative Models: A
  Review
Sahil Sidheekh Sriraam Natarajan
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.