{"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-with-random-partition","title":"Parallelizing MCMC with Random Partition Trees","arxiv_id":"1506.03164","date":"2015-06-10","proceeding":"NeurIPS 2015 12","authors":["Xiangyu Wang","Fangjian Guo","Katherine A. Heller","David B. Dunson"],"abstract":"The modern scale of data has brought new challenges to Bayesian inference. In\nparticular, conventional MCMC algorithms are computationally very expensive for\nlarge data sets. A promising approach to solve this problem is embarrassingly\nparallel MCMC (EP-MCMC), which first partitions the data into multiple subsets\nand runs independent sampling algorithms on each subset. The subset posterior\ndraws are then aggregated via some combining rules to obtain the final\napproximation. Existing EP-MCMC algorithms are limited by approximation\naccuracy and difficulty in resampling. In this article, we propose a new\nEP-MCMC algorithm PART that solves these problems. The new algorithm applies\nrandom partition trees to combine the subset posterior draws, which is\ndistribution-free, easy to resample from and can adapt to multiple scales. We\nprovide theoretical justification and extensive experiments illustrating\nempirical performance.","url_abs":"http://arxiv.org/abs/1506.03164v2","url_pdf":"http://arxiv.org/pdf/1506.03164v2.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-with-random-partition","repo_url":"https://github.com/richardkwo/random-tree-parallel-MCMC","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"parallelizing-mcmc-with-random-partition","repo_url":"https://github.com/wwrechard/random-tree-parallel-MCMC","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"bayesian-inference","task_name":"Bayesian Inference"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1506.03164","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}