Poisson–Gamma Dynamical Systems
- Aaron Schein ,
- Hanna Wallach ,
- Mingyuan Zhou
Advances in Neural Information Processing Systems 29 (NIPS 2016) |
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.