{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/poisson-gamma-dynamical-systems-1","title":"Poisson-Gamma dynamical systems","arxiv_id":null,"date":"2016-12-01","proceeding":"NeurIPS 2016 12","authors":["Aaron Schein","Hanna Wallach","Mingyuan Zhou"],"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.","url_abs":"http://papers.nips.cc/paper/6083-poisson-gamma-dynamical-systems","url_pdf":"http://papers.nips.cc/paper/6083-poisson-gamma-dynamical-systems.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"poisson-gamma-dynamical-systems-1","repo_url":"https://github.com/aschein/pgds","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":{"status":"ok"}}],"tasks":[{"task_slug":"inductive-bias","task_name":"Inductive Bias"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}