{"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/recurrent-switching-linear-dynamical-systems","title":"Recurrent switching linear dynamical systems","arxiv_id":"1610.08466","date":"2016-10-26","proceeding":null,"authors":["Scott W. Linderman","Andrew C. Miller","Ryan P. Adams","David M. Blei","Liam Paninski","Matthew J. Johnson"],"abstract":"Many natural systems, such as neurons firing in the brain or basketball teams\ntraversing a court, give rise to time series data with complex, nonlinear\ndynamics. We can gain insight into these systems by decomposing the data into\nsegments that are each explained by simpler dynamic units. Building on\nswitching linear dynamical systems (SLDS), we present a new model class that\nnot only discovers these dynamical units, but also explains how their switching\nbehavior depends on observations or continuous latent states. These \"recurrent\"\nswitching linear dynamical systems provide further insight by discovering the\nconditions under which each unit is deployed, something that traditional SLDS\nmodels fail to do. We leverage recent algorithmic advances in approximate\ninference to make Bayesian inference in these models easy, fast, and scalable.","url_abs":"http://arxiv.org/abs/1610.08466v1","url_pdf":"http://arxiv.org/pdf/1610.08466v1.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":"recurrent-switching-linear-dynamical-systems","repo_url":"https://github.com/slinderman/pypolyagamma","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"unanswered"}}],"tasks":[{"task_slug":"bayesian-inference","task_name":"Bayesian Inference"},{"task_slug":"time-series-1","task_name":"Time Series"},{"task_slug":"time-series","task_name":"Time Series Analysis"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/1610.08466","atlas_url":"https://app.syntology.ai/?focus=1610.08466","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}