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Poisson-Gamma dynamical systems
Aaron Schein · Hanna Wallach · Mingyuan Zhou

Wed Dec 07 02:20 AM -- 02:40 AM (PST) @ Area 3
in Algorithms »

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.

Keywords: (Other) Unsupervised Learning Methods · (Other) Probabilistic Models and Methods · (Other) Bayesian Inference
[ Video] 

Author Information

Aaron Schein (UMass Amherst)
Hanna Wallach (Microsoft Research)
Mingyuan Zhou (University of Texas at Austin)

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