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

Wed Dec 09:00 AM -- 12:30 PM PST @ Area 5+6+7+8 #193 #None
in Wednesday Posters »

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) Probabilistic Models and Methods · (Other) Bayesian Inference · (Other) Unsupervised Learning Methods

Author Information

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

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