{"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/graphical-model-inference-sequential-monte","title":"Graphical model inference: Sequential Monte Carlo meets deterministic approximations","arxiv_id":"1901.02374","date":"2019-01-08","proceeding":"NeurIPS 2018 12","authors":["Fredrik Lindsten","Jouni Helske","Matti Vihola"],"abstract":"Approximate inference in probabilistic graphical models (PGMs) can be grouped\ninto deterministic methods and Monte-Carlo-based methods. The former can often\nprovide accurate and rapid inferences, but are typically associated with biases\nthat are hard to quantify. The latter enjoy asymptotic consistency, but can\nsuffer from high computational costs. In this paper we present a way of\nbridging the gap between deterministic and stochastic inference. Specifically,\nwe suggest an efficient sequential Monte Carlo (SMC) algorithm for PGMs which\ncan leverage the output from deterministic inference methods. While generally\napplicable, we show explicitly how this can be done with loopy belief\npropagation, expectation propagation, and Laplace approximations. The resulting\nalgorithm can be viewed as a post-correction of the biases associated with\nthese methods and, indeed, numerical results show clear improvements over the\nbaseline deterministic methods as well as over \"plain\" SMC.","url_abs":"http://arxiv.org/abs/1901.02374v1","url_pdf":"http://arxiv.org/pdf/1901.02374v1.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":"graphical-model-inference-sequential-monte","repo_url":"https://github.com/freli005/smc-pgm-twist","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null},{"paper_slug":"graphical-model-inference-sequential-monte","repo_url":"https://github.com/helske/particlefield","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}