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/}
}