{"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/binary-bouncy-particle-sampler","title":"Binary Bouncy Particle Sampler","arxiv_id":"1711.00922","date":"2017-11-02","proceeding":null,"authors":["Ari Pakman"],"abstract":"The Bouncy Particle Sampler is a novel rejection-free non-reversible sampler\nfor differentiable probability distributions over continuous variables. We\ngeneralize the algorithm to piecewise differentiable distributions and apply it\nto generic binary distributions using a piecewise differentiable augmentation.\nWe illustrate the new algorithm in a binary Markov Random Field example, and\ncompare it to binary Hamiltonian Monte Carlo. Our results suggest that binary\nBPS samplers are better for easy to mix distributions.","url_abs":"http://arxiv.org/abs/1711.00922v1","url_pdf":"http://arxiv.org/pdf/1711.00922v1.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":"binary-bouncy-particle-sampler","repo_url":"https://github.com/aripakman/binary_bps","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}