{"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/parallelizing-mcmc-via-weierstrass-sampler","title":"Parallelizing MCMC via Weierstrass Sampler","arxiv_id":"1312.4605","date":"2013-12-17","proceeding":null,"authors":["Xiangyu Wang","David B. Dunson"],"abstract":"With the rapidly growing scales of statistical problems, subset based\ncommunication-free parallel MCMC methods are a promising future for large scale\nBayesian analysis. In this article, we propose a new Weierstrass sampler for\nparallel MCMC based on independent subsets. The new sampler approximates the\nfull data posterior samples via combining the posterior draws from independent\nsubset MCMC chains, and thus enjoys a higher computational efficiency. We show\nthat the approximation error for the Weierstrass sampler is bounded by some\ntuning parameters and provide suggestions for choice of the values. Simulation\nstudy shows the Weierstrass sampler is very competitive compared to other\nmethods for combining MCMC chains generated for subsets, including averaging\nand kernel smoothing.","url_abs":"http://arxiv.org/abs/1312.4605v2","url_pdf":"http://arxiv.org/pdf/1312.4605v2.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":"parallelizing-mcmc-via-weierstrass-sampler","repo_url":"https://github.com/wwrechard/weierstrass","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok"}}],"tasks":[{"task_slug":"computational-efficiency","task_name":"Computational Efficiency"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1312.4605","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}