PMID- 20740057 OWN - NLM STAT- PubMed-not-MEDLINE LR - 20240318 IS - 1076-9757 (Print) IS - 1943-5037 (Electronic) IS - 1076-9757 (Linking) VI - 37 DP - 2010 Jan 1 TI - Join-Graph Propagation Algorithms. PG - 279-328 AB - The paper investigates parameterized approximate message-passing schemes that are based on bounded inference and are inspired by Pearl's belief propagation algorithm (BP). We start with the bounded inference mini-clustering algorithm and then move to the iterative scheme called Iterative Join-Graph Propagation (IJGP), that combines both iteration and bounded inference. Algorithm IJGP belongs to the class of Generalized Belief Propagation algorithms, a framework that allowed connections with approximate algorithms from statistical physics and is shown empirically to surpass the performance of mini-clustering and belief propagation, as well as a number of other state-of-the-art algorithms on several classes of networks. We also provide insight into the accuracy of iterative BP and IJGP by relating these algorithms to well known classes of constraint propagation schemes. FAU - Mateescu, Robert AU - Mateescu R AD - Microsoft Research 7 J J Thomson Avenue Cambridge CB3 0FB, UK ROMATEES@MICROSOFT.COM. FAU - Kask, Kalev AU - Kask K FAU - Gogate, Vibhav AU - Gogate V FAU - Dechter, Rina AU - Dechter R LA - eng GR - R01 HG004175/HG/NHGRI NIH HHS/United States GR - R01 HG004175-02/HG/NHGRI NIH HHS/United States PT - Journal Article PL - United States TA - J Artif Intell Res JT - The journal of artificial intelligence research JID - 101512254 PMC - PMC2926991 MID - NIHMS220551 EDAT- 2010/08/27 06:00 MHDA- 2010/08/27 06:01 PMCR- 2010/08/24 CRDT- 2010/08/27 06:00 PHST- 2010/08/27 06:00 [entrez] PHST- 2010/08/27 06:00 [pubmed] PHST- 2010/08/27 06:01 [medline] PHST- 2010/08/24 00:00 [pmc-release] AID - 10.1613/jair.2842 [doi] PST - ppublish SO - J Artif Intell Res. 2010 Jan 1;37:279-328. doi: 10.1613/jair.2842.