{"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/bayesian-conditional-density-filtering","title":"Bayesian Conditional Density Filtering","arxiv_id":"1401.3632","date":"2014-01-15","proceeding":null,"authors":["Shaan Qamar","Rajarshi Guhaniyogi","David B. Dunson"],"abstract":"We propose a Conditional Density Filtering (C-DF) algorithm for efficient\nonline Bayesian inference. C-DF adapts MCMC sampling to the online setting,\nsampling from approximations to conditional posterior distributions obtained by\npropagating surrogate conditional sufficient statistics (a function of data and\nparameter estimates) as new data arrive. These quantities eliminate the need to\nstore or process the entire dataset simultaneously and offer a number of\ndesirable features. Often, these include a reduction in memory requirements and\nruntime and improved mixing, along with state-of-the-art parameter inference\nand prediction. These improvements are demonstrated through several\nillustrative examples including an application to high dimensional compressed\nregression. Finally, we show that C-DF samples converge to the target posterior\ndistribution asymptotically as sampling proceeds and more data arrives.","url_abs":"http://arxiv.org/abs/1401.3632v3","url_pdf":"http://arxiv.org/pdf/1401.3632v3.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":"bayesian-conditional-density-filtering","repo_url":"https://github.com/rajguhaniyogi/Bayesian-Conditional-Density-Filtering","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"bayesian-inference","task_name":"Bayesian Inference"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}