{"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/black-box-variational-inference-for","title":"Black-box Variational Inference for Stochastic Differential Equations","arxiv_id":"1802.03335","date":"2018-02-09","proceeding":"ICML 2018 7","authors":["Thomas Ryder","Andrew Golightly","A. Stephen McGough","Dennis Prangle"],"abstract":"Parameter inference for stochastic differential equations is challenging due\nto the presence of a latent diffusion process. Working with an Euler-Maruyama\ndiscretisation for the diffusion, we use variational inference to jointly learn\nthe parameters and the diffusion paths. We use a standard mean-field\nvariational approximation of the parameter posterior, and introduce a recurrent\nneural network to approximate the posterior for the diffusion paths conditional\non the parameters. This neural network learns how to provide Gaussian state\ntransitions which bridge between observations in a very similar way to the\nconditioned diffusion process. The resulting black-box inference method can be\napplied to any SDE system with light tuning requirements. We illustrate the\nmethod on a Lotka-Volterra system and an epidemic model, producing accurate\nparameter estimates in a few hours.","url_abs":"http://arxiv.org/abs/1802.03335v3","url_pdf":"http://arxiv.org/pdf/1802.03335v3.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":"black-box-variational-inference-for","repo_url":"https://github.com/Tom-Ryder/VIforSDEs","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"tf","reach":{"status":"unanswered"}},{"paper_slug":"black-box-variational-inference-for","repo_url":"https://github.com/anonmaths/tumour_sde_model","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"unanswered"}}],"tasks":[{"task_slug":"variational-inference","task_name":"Variational Inference"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1802.03335","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}