{"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/population-empirical-bayes","title":"Population Empirical Bayes","arxiv_id":"1411.0292","date":"2014-11-02","proceeding":null,"authors":["Alp Kucukelbir","David M. Blei"],"abstract":"Bayesian predictive inference analyzes a dataset to make predictions about\nnew observations. When a model does not match the data, predictive accuracy\nsuffers. We develop population empirical Bayes (POP-EB), a hierarchical\nframework that explicitly models the empirical population distribution as part\nof Bayesian analysis. We introduce a new concept, the latent dataset, as a\nhierarchical variable and set the empirical population as its prior. This leads\nto a new predictive density that mitigates model mismatch. We efficiently apply\nthis method to complex models by proposing a stochastic variational inference\nalgorithm, called bumping variational inference (BUMP-VI). We demonstrate\nimproved predictive accuracy over classical Bayesian inference in three models:\na linear regression model of health data, a Bayesian mixture model of natural\nimages, and a latent Dirichlet allocation topic model of scientific documents.","url_abs":"http://arxiv.org/abs/1411.0292v2","url_pdf":"http://arxiv.org/pdf/1411.0292v2.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":"population-empirical-bayes","repo_url":"https://github.com/Blei-Lab/lda-bump-cpp","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":{"status":"ok","spdx":"GPL-2.0"}}],"tasks":[{"task_slug":"bayesian-inference","task_name":"Bayesian Inference"},{"task_slug":"variational-inference","task_name":"Variational Inference"},{"task_slug":"regression-1","task_name":"regression"}],"methods":[{"method_slug":"linear-regression","method_name":"Linear Regression"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1411.0292","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}