Poisson--Gamma Dynamical Systems
This work addresses the problem of overfitting in count data modeling for researchers and practitioners, presenting an incremental improvement with a novel Bayesian nonparametric prior.
The authors tackled modeling sequentially observed multivariate count data by introducing a Poisson-Gamma dynamical system, which demonstrated superior predictive performance and inferred highly interpretable latent structure on real-world datasets.
We introduce a new dynamical system for sequentially observed multivariate count data. This model is based on the gamma--Poisson construction---a natural choice for count data---and relies on a novel Bayesian nonparametric prior that ties and shrinks the model parameters, thus avoiding overfitting. We present an efficient MCMC inference algorithm that advances recent work on augmentation schemes for inference in negative binomial models. Finally, we demonstrate the model's inductive bias using a variety of real-world data sets, showing that it exhibits superior predictive performance over other models and infers highly interpretable latent structure.