Tractable Operations for Arithmetic Circuits of Probabilistic Models
Yujia Shen, Arthur Choi, Adnan Darwiche
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.
BibTeX
@inproceedings{NIPS2016_5a7f963e,
author = {Shen, Yujia and Choi, Arthur and Darwiche, Adnan},
booktitle = {Advances in Neural Information Processing Systems},
editor = {D. Lee and M. Sugiyama and U. Luxburg and I. Guyon and R. Garnett},
pages = {},
publisher = {Curran Associates, Inc.},
title = {Tractable Operations for Arithmetic Circuits of Probabilistic Models},
url = {https://proceedings.neurips.cc/paper_files/paper/2016/file/5a7f963e5e0504740c3a6b10bb6d4fa5-Paper.pdf},
volume = {29},
year = {2016}
}