{"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/expectation-particle-belief-propagation","title":"Expectation Particle Belief Propagation","arxiv_id":"1506.05934","date":"2015-06-19","proceeding":"NeurIPS 2015 12","authors":["Thibaut Lienart","Yee Whye Teh","Arnaud Doucet"],"abstract":"We propose an original particle-based implementation of the Loopy Belief\nPropagation (LPB) algorithm for pairwise Markov Random Fields (MRF) on a\ncontinuous state space. The algorithm constructs adaptively efficient proposal\ndistributions approximating the local beliefs at each note of the MRF. This is\nachieved by considering proposal distributions in the exponential family whose\nparameters are updated iterately in an Expectation Propagation (EP) framework.\nThe proposed particle scheme provides consistent estimation of the LBP\nmarginals as the number of particles increases. We demonstrate that it provides\nmore accurate results than the Particle Belief Propagation (PBP) algorithm of\nIhler and McAllester (2009) at a fraction of the computational cost and is\nadditionally more robust empirically. The computational complexity of our\nalgorithm at each iteration is quadratic in the number of particles. We also\npropose an accelerated implementation with sub-quadratic computational\ncomplexity which still provides consistent estimates of the loopy BP marginal\ndistributions and performs almost as well as the original procedure.","url_abs":"http://arxiv.org/abs/1506.05934v1","url_pdf":"http://arxiv.org/pdf/1506.05934v1.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":"expectation-particle-belief-propagation","repo_url":"https://github.com/tlienart/EPBP","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}