{"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/variational-sequential-monte-carlo","title":"Variational Sequential Monte Carlo","arxiv_id":"1705.11140","date":"2017-05-31","proceeding":null,"authors":["Christian A. Naesseth","Scott W. Linderman","Rajesh Ranganath","David M. Blei"],"abstract":"Many recent advances in large scale probabilistic inference rely on\nvariational methods. The success of variational approaches depends on (i)\nformulating a flexible parametric family of distributions, and (ii) optimizing\nthe parameters to find the member of this family that most closely approximates\nthe exact posterior. In this paper we present a new approximating family of\ndistributions, the variational sequential Monte Carlo (VSMC) family, and show\nhow to optimize it in variational inference. VSMC melds variational inference\n(VI) and sequential Monte Carlo (SMC), providing practitioners with flexible,\naccurate, and powerful Bayesian inference. The VSMC family is a variational\nfamily that can approximate the posterior arbitrarily well, while still\nallowing for efficient optimization of its parameters. We demonstrate its\nutility on state space models, stochastic volatility models for financial data,\nand deep Markov models of brain neural circuits.","url_abs":"http://arxiv.org/abs/1705.11140v2","url_pdf":"http://arxiv.org/pdf/1705.11140v2.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":"variational-sequential-monte-carlo","repo_url":"https://github.com/blei-lab/variational-smc","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":{"status":"unanswered"}}],"tasks":[{"task_slug":"bayesian-inference","task_name":"Bayesian Inference"},{"task_slug":"state-space-models","task_name":"State Space Models"},{"task_slug":"variational-inference","task_name":"Variational Inference"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1705.11140","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}