Tractable Operations for Arithmetic Circuits of Probabilistic Models

Abstract

We consider tractable representations of probability distributions and the polytime operations they support. In particular, we consider a recently proposed arithmetic circuit representation, the Probabilistic Sentential Decision Diagram (PSDD). We show that PSDD supports a polytime multiplication operator, while they do not support a polytime operator for summing-out variables. A polytime multiplication operator make PSDDs suitable for a broader class of applications compared to arithmetic circuits, which do not in general support multiplication. As one example, we show that PSDD multiplication leads to a very simple but effective compilation algorithm for probabilistic graphical models: represent each model factor as a PSDD, and then multiply them.

Cite

Text

Shen et al. "Tractable Operations for Arithmetic Circuits of Probabilistic Models." Neural Information Processing Systems, 2016.

Markdown

[Shen et al. "Tractable Operations for Arithmetic Circuits of Probabilistic Models." Neural Information Processing Systems, 2016.](https://mlanthology.org/neurips/2016/shen2016neurips-tractable/)

BibTeX

@inproceedings{shen2016neurips-tractable,
  title     = {{Tractable Operations for Arithmetic Circuits of Probabilistic Models}},
  author    = {Shen, Yujia and Choi, Arthur and Darwiche, Adnan},
  booktitle = {Neural Information Processing Systems},
  year      = {2016},
  pages     = {3936-3944},
  url       = {https://mlanthology.org/neurips/2016/shen2016neurips-tractable/}
}