Beta-Negative Binomial Process and Poisson Factor Analysis
Abstract
A beta-negative binomial (BNB) process is proposed, leading to a beta-gamma-Poisson process, which may be viewed as a “multi-scoop” generalization of the beta-Bernoulli process. The BNB process is augmented into a beta-gamma-gamma-Poisson hierarchical structure, and applied as a nonparametric Bayesian prior for an infinite Poisson factor analysis model. A finite approximation for the beta process Levy random measure is constructed for convenient implementation. Efficient MCMC computations are performed with data augmentation and marginalization techniques. Encouraging results are shown on document count matrix factorization.
Cite
Text
Zhou et al. "Beta-Negative Binomial Process and Poisson Factor Analysis." Proceedings of the Fifteenth International Conference on Artificial Intelligence and Statistics, 2012.Markdown
[Zhou et al. "Beta-Negative Binomial Process and Poisson Factor Analysis." Proceedings of the Fifteenth International Conference on Artificial Intelligence and Statistics, 2012.](https://mlanthology.org/aistats/2012/zhou2012aistats-betanegative/)BibTeX
@inproceedings{zhou2012aistats-betanegative,
title = {{Beta-Negative Binomial Process and Poisson Factor Analysis}},
author = {Zhou, Mingyuan and Hannah, Lauren and Dunson, David and Carin, Lawrence},
booktitle = {Proceedings of the Fifteenth International Conference on Artificial Intelligence and Statistics},
year = {2012},
pages = {1462-1471},
volume = {22},
url = {https://mlanthology.org/aistats/2012/zhou2012aistats-betanegative/}
}