{"month":"09","oa":1,"author":[{"last_name":"Kolmogorov","first_name":"Vladimir","id":"3D50B0BA-F248-11E8-B48F-1D18A9856A87","full_name":"Vladimir Kolmogorov"}],"citation":{"ama":"Kolmogorov V. <i>Reweighted Message Passing Revisited</i>. IST Austria; 2013.","chicago":"Kolmogorov, Vladimir. <i>Reweighted Message Passing Revisited</i>. IST Austria, 2013.","mla":"Kolmogorov, Vladimir. <i>Reweighted Message Passing Revisited</i>. IST Austria, 2013.","apa":"Kolmogorov, V. (2013). <i>Reweighted message passing revisited</i>. IST Austria.","ista":"Kolmogorov V. 2013. Reweighted message passing revisited, IST Austria,p.","ieee":"V. Kolmogorov, <i>Reweighted message passing revisited</i>. IST Austria, 2013.","short":"V. Kolmogorov, Reweighted Message Passing Revisited, IST Austria, 2013."},"publisher":"IST Austria","abstract":[{"lang":"eng","text":"We propose a new family of message passing techniques for MAP estimation in graphical models which we call Sequential Reweighted Message Passing (SRMP). Special cases include well-known techniques such as Min-Sum Diusion (MSD) and a faster Sequential Tree-Reweighted Message Passing (TRW-S). Importantly, our derivation is simpler than the original derivation of TRW-S, and does not involve a  decomposition into trees. This allows easy generalizations. We present such a generalization for the case of higher-order graphical models, and test it on several real-world problems with promising results."}],"date_created":"2018-12-11T11:56:42Z","title":"Reweighted message passing revisited","date_published":"2013-09-22T00:00:00Z","type":"report","department":[{"_id":"VlKo"}],"quality_controlled":0,"main_file_link":[{"url":"http://arxiv.org/abs/1309.5655","open_access":"1"}],"publication_status":"published","publist_id":"4671","status":"public","date_updated":"2019-01-24T13:07:32Z","day":"22","extern":0,"year":"2013","_id":"2273"}