Show simple item record Daly, Ronan Shen, Qiang Aitken, Stuart 2011-06-08T14:11:17Z 2011-06-08T14:11:17Z 2011-05
dc.identifier.citation Daly , R , Shen , Q & Aitken , S 2011 , ' Learning Bayesian networks: approaches and issues ' Knowledge Engineering Review , vol 26 , no. 2 , pp. 99-157 . DOI: 10.1017/S0269888910000251 en
dc.identifier.issn 1469-8005
dc.identifier.other PURE: 161334
dc.identifier.other PURE UUID: 70ed08c2-742d-4c01-9059-f75e4f81e029
dc.identifier.other dspace: 2160/7008
dc.identifier.other RAD_Outputs_All_ID_Import_20121105.csv: row: 3809
dc.identifier.other Scopus: 79958782255
dc.description Daly, R., Shen, Q., Aitken, S. (2011). Learning Bayesian networks: approaches and issues. Knowledge Engineering Review, 26 (2), 99-157 en
dc.description.abstract Bayesian networks have become a widely used method in the modelling of uncertain knowledge. Owing to the difficulty domain experts have in specifying them, techniques that learn Bayesian networks from data have become indispensable. Recently, however, there have been many important new developments in this field. This work takes a broad look at the literature on learning Bayesian networks—in particular their structure—from data. Specific topics are not focused on in detail, but it is hoped that all the major fields in the area are covered. This article is not intended to be a tutorial—for this, there are many books on the topic, which will be presented. However, an effort has been made to locate all the relevant publications, so that this paper can be used as a ready reference to find the works on particular sub-topics. en
dc.format.extent 59 en
dc.language.iso eng
dc.relation.ispartof Knowledge Engineering Review en
dc.rights en
dc.title Learning Bayesian networks: approaches and issues en
dc.type /dk/atira/pure/researchoutput/researchoutputtypes/contributiontojournal/article en
dc.contributor.institution Department of Computer Science en
dc.contributor.institution Advanced Reasoning Group en
dc.description.status Peer reviewed en

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