NeurIPS 2016oral64 citations
Poisson-Gamma dynamical systems
Aaron Schein, Hanna Wallach, Mingyuan Zhou
Abstract
This paper presents a dynamical system based on the Poisson-Gamma construction for sequentially observed multivariate count data. Inherent to the model is a novel Bayesian nonparametric prior that ties and shrinks parameters in a powerful way. We develop theory about the model's infinite limit and its steady-state. The model's inductive bias is demonstrated on a variety of real-world datasets where it is shown to learn interpretable structure and have superior predictive performance.
BibTeX
@inproceedings{NIPS2016_8169e05e,
author = {Schein, Aaron and Wallach, Hanna and Zhou, Mingyuan},
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 = {Poisson-Gamma dynamical systems},
url = {https://proceedings.neurips.cc/paper_files/paper/2016/file/8169e05e2a0debcb15458f2cc1eff0ea-Paper.pdf},
volume = {29},
year = {2016}
}