Poisson-Gamma Dynamical Systems
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.
Cite
Text
Schein et al. "Poisson-Gamma Dynamical Systems." Neural Information Processing Systems, 2016.Markdown
[Schein et al. "Poisson-Gamma Dynamical Systems." Neural Information Processing Systems, 2016.](https://mlanthology.org/neurips/2016/schein2016neurips-poissongamma/)BibTeX
@inproceedings{schein2016neurips-poissongamma,
title = {{Poisson-Gamma Dynamical Systems}},
author = {Schein, Aaron and Wallach, Hanna and Zhou, Mingyuan},
booktitle = {Neural Information Processing Systems},
year = {2016},
pages = {5005-5013},
url = {https://mlanthology.org/neurips/2016/schein2016neurips-poissongamma/}
}