{"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/correlated-pseudo-marginal-metropolis","title":"Correlated pseudo-marginal Metropolis-Hastings using quasi-Newton proposals","arxiv_id":"1806.09780","date":"2018-06-26","proceeding":null,"authors":["Johan Dahlin","Adrian Wills","Brett Ninness"],"abstract":"Pseudo-marginal Metropolis-Hastings (pmMH) is a versatile algorithm for\nsampling from target distributions which are not easy to evaluate point-wise.\nHowever, pmMH requires good proposal distributions to sample efficiently from\nthe target, which can be problematic to construct in practice. This is\nespecially a problem for high-dimensional targets when the standard random-walk\nproposal is inefficient. We extend pmMH to allow for constructing the proposal\nbased on information from multiple past iterations. As a consequence,\nquasi-Newton (qN) methods can be employed to form proposals which utilize\ngradient information to guide the Markov chain to areas of high probability and\nto construct approximations of the local curvature to scale step sizes. The\nproposed method is demonstrated on several problems which indicate that qN\nproposals can perform better than other common Hessian-based proposals.","url_abs":"http://arxiv.org/abs/1806.09780v2","url_pdf":"http://arxiv.org/pdf/1806.09780v2.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":"correlated-pseudo-marginal-metropolis","repo_url":"https://github.com/compops/pmmh-qn","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}