{"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-state","title":"Black box variational inference for state space models","arxiv_id":"1511.07367","date":"2015-11-23","proceeding":null,"authors":["Evan Archer","Il Memming Park","Lars Buesing","John Cunningham","Liam Paninski"],"abstract":"Latent variable time-series models are among the most heavily used tools from\nmachine learning and applied statistics. These models have the advantage of\nlearning latent structure both from noisy observations and from the temporal\nordering in the data, where it is assumed that meaningful correlation structure\nexists across time. A few highly-structured models, such as the linear\ndynamical system with linear-Gaussian observations, have closed-form inference\nprocedures (e.g. the Kalman Filter), but this case is an exception to the\ngeneral rule that exact posterior inference in more complex generative models\nis intractable. Consequently, much work in time-series modeling focuses on\napproximate inference procedures for one particular class of models. Here, we\nextend recent developments in stochastic variational inference to develop a\n`black-box' approximate inference technique for latent variable models with\nlatent dynamical structure. We propose a structured Gaussian variational\napproximate posterior that carries the same intuition as the standard Kalman\nfilter-smoother but, importantly, permits us to use the same inference approach\nto approximate the posterior of much more general, nonlinear latent variable\ngenerative models. We show that our approach recovers accurate estimates in the\ncase of basic models with closed-form posteriors, and more interestingly\nperforms well in comparison to variational approaches that were designed in a\nbespoke fashion for specific non-conjugate models.","url_abs":"http://arxiv.org/abs/1511.07367v1","url_pdf":"http://arxiv.org/pdf/1511.07367v1.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-state","repo_url":"https://github.com/earcher/vilds","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok"}}],"tasks":[{"task_slug":"state-space-models","task_name":"State Space Models"},{"task_slug":"time-series-1","task_name":"Time Series"},{"task_slug":"time-series","task_name":"Time Series Analysis"},{"task_slug":"variational-inference","task_name":"Variational Inference"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1511.07367","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}