{"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/quasi-newton-particle-metropolis-hastings","title":"Quasi-Newton particle Metropolis-Hastings","arxiv_id":"1502.03656","date":"2015-02-12","proceeding":null,"authors":["Johan Dahlin","Fredrik Lindsten","Thomas B. Schön"],"abstract":"Particle Metropolis-Hastings enables Bayesian parameter inference in general\nnonlinear state space models (SSMs). However, in many implementations a random\nwalk proposal is used and this can result in poor mixing if not tuned correctly\nusing tedious pilot runs. Therefore, we consider a new proposal inspired by\nquasi-Newton algorithms that may achieve similar (or better) mixing with less\ntuning. An advantage compared to other Hessian based proposals, is that it only\nrequires estimates of the gradient of the log-posterior. A possible application\nis parameter inference in the challenging class of SSMs with intractable\nlikelihoods. We exemplify this application and the benefits of the new proposal\nby modelling log-returns of future contracts on coffee by a stochastic\nvolatility model with $\\alpha$-stable observations.","url_abs":"http://arxiv.org/abs/1502.03656v2","url_pdf":"http://arxiv.org/pdf/1502.03656v2.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":"quasi-newton-particle-metropolis-hastings","repo_url":"https://github.com/compops/qpmh2-sysid2015","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"state-space-models","task_name":"State Space Models"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}