{"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/bayesian-optimisation-for-fast-approximate","title":"Bayesian optimisation for fast approximate inference in state-space models with intractable likelihoods","arxiv_id":"1506.06975","date":"2015-06-23","proceeding":null,"authors":["Johan Dahlin","Mattias Villani","Thomas B. Schön"],"abstract":"We consider the problem of approximate Bayesian parameter inference in\nnon-linear state-space models with intractable likelihoods. Sequential Monte\nCarlo with approximate Bayesian computations (SMC-ABC) is one approach to\napproximate the likelihood in this type of models. However, such approximations\ncan be noisy and computationally costly which hinders efficient implementations\nusing standard methods based on optimisation and Monte Carlo methods. We\npropose a computationally efficient novel method based on the combination of\nGaussian process optimisation and SMC-ABC to create a Laplace approximation of\nthe intractable posterior. We exemplify the proposed algorithm for inference in\nstochastic volatility models with both synthetic and real-world data as well as\nfor estimating the Value-at-Risk for two portfolios using a copula model. We\ndocument speed-ups of between one and two orders of magnitude compared to\nstate-of-the-art algorithms for posterior inference.","url_abs":"http://arxiv.org/abs/1506.06975v3","url_pdf":"http://arxiv.org/pdf/1506.06975v3.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":"bayesian-optimisation-for-fast-approximate","repo_url":"https://github.com/compops/gpo-abc2015","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null},{"paper_slug":"bayesian-optimisation-for-fast-approximate","repo_url":"https://github.com/compops/gpo-smc-abc","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"bayesian-optimisation","task_name":"Bayesian Optimisation"},{"task_slug":"state-space-models","task_name":"State Space Models"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}