{"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/kernel-sequential-monte-carlo","title":"Kernel Sequential Monte Carlo","arxiv_id":"1510.03105","date":"2015-10-11","proceeding":null,"authors":["Ingmar Schuster","Heiko Strathmann","Brooks Paige","Dino Sejdinovic"],"abstract":"We propose kernel sequential Monte Carlo (KSMC), a framework for sampling\nfrom static target densities. KSMC is a family of sequential Monte Carlo\nalgorithms that are based on building emulator models of the current particle\nsystem in a reproducing kernel Hilbert space. We here focus on modelling\nnonlinear covariance structure and gradients of the target. The emulator's\ngeometry is adaptively updated and subsequently used to inform local proposals.\nUnlike in adaptive Markov chain Monte Carlo, continuous adaptation does not\ncompromise convergence of the sampler. KSMC combines the strengths of sequental\nMonte Carlo and kernel methods: superior performance for multimodal targets and\nthe ability to estimate model evidence as compared to Markov chain Monte Carlo,\nand the emulator's ability to represent targets that exhibit high degrees of\nnonlinearity. As KSMC does not require access to target gradients, it is\nparticularly applicable on targets whose gradients are unknown or prohibitively\nexpensive. We describe necessary tuning details and demonstrate the benefits of\nthe the proposed methodology on a series of challenging synthetic and\nreal-world examples.","url_abs":"http://arxiv.org/abs/1510.03105v4","url_pdf":"http://arxiv.org/pdf/1510.03105v4.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":"kernel-sequential-monte-carlo","repo_url":"https://github.com/ingmarschuster/kameleon_rks","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[],"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}