{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,4]],"date-time":"2025-03-04T21:10:11Z","timestamp":1741122611245,"version":"3.38.0"},"reference-count":27,"publisher":"World Scientific Pub Co Pte Ltd","issue":"02","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Int. J. Unc. Fuzz. Knowl. Based Syst."],"published-print":{"date-parts":[[2011,4]]},"abstract":"<jats:p>When a Bayesian network (BN) is modified, for example adding or deleting a node, or changing the probability distributions, we usually will need a total recompilation of the model, despite feeling that a partial (re)compilation could have been enough. Especially when considering dynamic models, in which variables are added and removed very frequently, these recompilations are quite resource consuming. But even further, for the task of building a model, which is in many occasions an iterative process, there is a clear lack of flexibility. When we use the term Incremental Compilation or IC we refer to the possibility of modifying a network and avoiding a complete recompilation to obtain the new (and different) join tree (JT). The main point we intend to study in this work is JT-based inference in Bayesian networks. Apart from undertaking the triangulation problem itself, we have achieved a great improvement for the compilation in BNs. We do not develop a new architecture for BNs inference, but taking some already existing framework JT-based for probability propagation such as Hugin or Shenoy and Shafer, we have designed a method that can be successfully applied to get better performance, as the experimental evaluation will show.<\/jats:p>","DOI":"10.1142\/s0218488511006952","type":"journal-article","created":{"date-parts":[[2011,4,4]],"date-time":"2011-04-04T09:53:17Z","timestamp":1301910797000},"page":"155-191","source":"Crossref","is-referenced-by-count":6,"title":["INCREMENTAL COMPILATION OF BAYESIAN NETWORKS BASED ON MAXIMAL PRIME SUBGRAPHS"],"prefix":"10.1142","volume":"19","author":[{"given":"M. JULIA","family":"FLORES","sequence":"first","affiliation":[{"name":"Computing Systems Department, University of Castilla-La Mancha, Campus Universitario s\/n, Albacete, 02071, Spain"}]},{"given":"JOSE A.","family":"G\u00c1MEZ","sequence":"additional","affiliation":[{"name":"Computing Systems Department, University of Castilla-La Mancha, Campus Universitario s\/n, Albacete, 02071, Spain"}]},{"given":"KRISTIAN G.","family":"OLESEN","sequence":"additional","affiliation":[{"name":"Computer Science Deparment, Aalborg University, Selma Lagerl\u00f6fs Vej 300, DK-9220 Aalborg, Denmark"}]}],"member":"219","published-online":{"date-parts":[[2011,11,21]]},"reference":[{"key":"rf2","doi-asserted-by":"publisher","DOI":"10.1016\/0166-218X(89)90031-0"},{"key":"rf3","first-page":"457","volume":"3040","author":"Bangs\u00f8 O.","journal-title":"Lecture Notes in Artificial Intelligence"},{"journal-title":"Lecture Notes in Computer Science","first-page":"98","author":"Cano A.","key":"rf4"},{"key":"rf5","doi-asserted-by":"publisher","DOI":"10.1002\/1098-111X(200011)15:11<1027::AID-INT4>3.0.CO;2-#"},{"key":"rf6","doi-asserted-by":"publisher","DOI":"10.1002\/net.10024"},{"key":"rf7","doi-asserted-by":"crossref","first-page":"155","DOI":"10.1613\/jair.764","volume":"13","author":"Cheng J.","journal-title":"Journal of Artificial Intelligence Research (JAIR)"},{"key":"rf12","doi-asserted-by":"publisher","DOI":"10.1002\/int.10079"},{"key":"rf13","unstructured":"M. J.\u00a0Flores and J. A.\u00a0G\u00e1mez, Advances in Probabilistic Graphical Models, Studies in Fuzziness and Soft Computing\u00a0213 (Springer, 2007)\u00a0pp. 117\u2013152."},{"key":"rf15","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-540-39879-0_6"},{"journal-title":"Probabilistic Graphical Models","first-page":"147","author":"Gonzales C.","key":"rf16"},{"key":"rf17","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijar.2009.04.011"},{"key":"rf19","first-page":"269","volume":"4","author":"Jensen F. V.","journal-title":"Computational Statistics Quarterly"},{"key":"rf20","doi-asserted-by":"publisher","DOI":"10.1007\/978-0-387-68282-2"},{"key":"rf21","doi-asserted-by":"publisher","DOI":"10.1214\/088342305000000304"},{"key":"rf25","doi-asserted-by":"crossref","first-page":"157","DOI":"10.1111\/j.2517-6161.1988.tb01721.x","volume":"50","author":"Lauritzen S. 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