{"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/ensemble-preconditioning-for-markov-chain","title":"Ensemble preconditioning for Markov chain Monte Carlo simulation","arxiv_id":"1607.03954","date":"2016-07-13","proceeding":null,"authors":["Charles Matthews","Jonathan Weare","Benedict Leimkuhler"],"abstract":"We describe parallel Markov chain Monte Carlo methods that propagate a\ncollective ensemble of paths, with local covariance information calculated from\nneighboring replicas. The use of collective dynamics eliminates multiplicative\nnoise and stabilizes the dynamics thus providing a practical approach to\ndifficult anisotropic sampling problems in high dimensions. Numerical\nexperiments with model problems demonstrate that dramatic potential speedups,\ncompared to various alternative schemes, are attainable.","url_abs":"http://arxiv.org/abs/1607.03954v1","url_pdf":"http://arxiv.org/pdf/1607.03954v1.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":"ensemble-preconditioning-for-markov-chain","repo_url":"https://bitbucket.org/c_matthews/ensembleqn","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}