{"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/importance-sampling-for-partially-observed","title":"Importance sampling for partially observed temporal epidemic models","arxiv_id":"1801.08244","date":"2018-08-15","proceeding":null,"authors":[],"abstract":"We present an importance sampling algorithm that can produce realisations of\nMarkovian epidemic models that exactly match observations, taken to be the\nnumber of a single event type over a period of time. The importance sampling\ncan be used to construct an efficient particle filter that targets the states\nof a system and hence estimate the likelihood to perform Bayesian parameter\ninference. When used in a particle marginal Metropolis Hastings scheme, the\nimportance sampling provides a large speed-up in terms of the effective sample\nsize per unit of computational time, compared to simple bootstrap sampling. The\nalgorithm is general, with minimal restrictions, and we show how it can be\napplied to any discrete-state continuous-time Markov chain where we wish to\nexactly match the number of a single event type over a period of time.","url_abs":"http://arxiv.org/abs/1801.08244v2","url_pdf":"http://arxiv.org/pdf/1801.08244v2.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":"importance-sampling-for-partially-observed","repo_url":"https://github.com/EpiStruct/Black-2018","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}