{"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/pf-a-c-library-for-fast-particle-filtering","title":"PF: A C++ Library for Fast Particle Filtering","arxiv_id":"2001.10451","date":"2020-01-28","proceeding":null,"authors":["Taylor R. Brown"],"abstract":"Particle filters are a class of algorithms that are used for \"tracking\" or \"filtering\" in real-time for a wide array of time series models. Despite their comprehensive applicability, particle filters are not always the tool of choice for many practitioners, due to how difficult they are to implement. This short article presents PF, a C++ header-only template library that provides fast implementations of many different particle filters. A tutorial along with an extensive fully-worked example is provided.","url_abs":"http://arxiv.org/abs/2001.10451v1","url_pdf":"http://arxiv.org/pdf/2001.10451v1.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":"links_only","authors_date_abstract":"arXiv metadata, CC0 1.0 (https://info.arxiv.org/help/license), from the Kaggle arXiv metadata snapshot of 2026-09-12"},"code_links":[{"paper_slug":"pf-a-c-library-for-fast-particle-filtering","repo_url":"https://github.com/tbrown122387/pf","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}