{"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","title":"Poisson--Gamma Dynamical Systems","arxiv_id":"1701.05573","date":"2017-01-19","proceeding":null,"authors":["Aaron Schein","Mingyuan Zhou","Hanna Wallach"],"abstract":"We introduce a new dynamical system for sequentially observed multivariate\ncount data. This model is based on the gamma--Poisson construction---a natural\nchoice for count data---and relies on a novel Bayesian nonparametric prior that\nties and shrinks the model parameters, thus avoiding overfitting. We present an\nefficient MCMC inference algorithm that advances recent work on augmentation\nschemes for inference in negative binomial models. Finally, we demonstrate the\nmodel's inductive bias using a variety of real-world data sets, showing that it\nexhibits superior predictive performance over other models and infers highly\ninterpretable latent structure.","url_abs":"http://arxiv.org/abs/1701.05573v1","url_pdf":"http://arxiv.org/pdf/1701.05573v1.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","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":"https://app.syntology.ai/?focus=1701.05573","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}