Learning Bayesian networks: approaches and issues

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dc.contributor.author Daly, Ronan
dc.contributor.author Shen, Qiang
dc.contributor.author Aitken, Stuart
dc.date.accessioned 2011-06-08T14:11:17Z
dc.date.available 2011-06-08T14:11:17Z
dc.date.issued 2011-05
dc.identifier.citation Daly , R , Shen , Q & Aitken , S 2011 , ' Learning Bayesian networks: approaches and issues ' The Knowledge Engineering Review , vol 26 , no. 2 , pp. 99-157 . en
dc.identifier.issn 1469-8005
dc.identifier.other PURE: 161334
dc.identifier.other dspace: 2160/7008
dc.identifier.uri http://hdl.handle.net/2160/7008
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 The Knowledge Engineering Review en
dc.title Learning Bayesian networks: approaches and issues en
dc.type Text en
dc.type.publicationtype Article (Journal) en
dc.identifier.doi http://dx.doi.org/10.1017/S0269888910000251
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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