Poisson-Gamma dynamical systems
Aaron Schein ⋅ Hanna Wallach ⋅ Mingyuan Zhou
Keywords:
(Other) Unsupervised Learning Methods
(Other) Probabilistic Models and Methods
(Other) Bayesian Inference
2016 Poster
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.
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