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