{"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/projected-stein-variational-newton-a-fast-and","title":"Projected Stein Variational Newton: A Fast and Scalable Bayesian Inference Method in High Dimensions","arxiv_id":null,"date":"2019-12-01","proceeding":"NeurIPS 2019 12","authors":["Peng Chen","Keyi Wu","Joshua Chen","Tom O'Leary-Roseberry","Omar Ghattas"],"abstract":"We propose a projected Stein variational Newton (pSVN) method for high-dimensional Bayesian inference. To address the curse of dimensionality, we exploit the intrinsic low-dimensional geometric structure of the posterior distribution in the high-dimensional parameter space via its Hessian (of the log posterior) operator and perform a parallel update of the parameter samples projected into a low-dimensional subspace by an SVN method. The subspace is adaptively constructed using the eigenvectors of the averaged Hessian at the current samples. We demonstrate fast convergence of the proposed method, complexity independent of the parameter and sample dimensions, and parallel scalability.","url_abs":"http://papers.nips.cc/paper/9649-projected-stein-variational-newton-a-fast-and-scalable-bayesian-inference-method-in-high-dimensions","url_pdf":"http://papers.nips.cc/paper/9649-projected-stein-variational-newton-a-fast-and-scalable-bayesian-inference-method-in-high-dimensions.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":"projected-stein-variational-newton-a-fast-and","repo_url":"https://github.com/cpempire/pSVN","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"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}