Large-scale empirical validation of Bayesian Network structure learning algorithms with noisy data.
dc.contributor.author | Constantinou, AC | en_US |
dc.contributor.author | Liu, Y | en_US |
dc.contributor.author | Chobtham, K | en_US |
dc.contributor.author | Guo, Z | en_US |
dc.contributor.author | Kitson, NK | en_US |
dc.date.accessioned | 2022-08-23T10:20:53Z | |
dc.date.issued | 2021 | en_US |
dc.identifier.uri | https://qmro.qmul.ac.uk/xmlui/handle/123456789/80100 | |
dc.format.extent | 151 - 188 | en_US |
dc.relation.ispartof | Int. J. Approx. Reason. | en_US |
dc.rights | This item is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. | |
dc.rights | Attribution 3.0 United States | * |
dc.rights.uri | http://creativecommons.org/licenses/by/3.0/us/ | * |
dc.title | Large-scale empirical validation of Bayesian Network structure learning algorithms with noisy data. | en_US |
dc.type | Article | |
dc.rights.holder | © 2021, The Author(s). Published by Elsevier Inc. | |
dc.identifier.doi | 10.1016/j.ijar.2021.01.001 | en_US |
pubs.notes | Not known | en_US |
pubs.volume | 131 | en_US |
rioxxterms.funder | Default funder | en_US |
rioxxterms.identifier.project | Default project | en_US |
qmul.funder | Bayesian Artificial Intelligence for Decision Making under Uncertainty::Engineering and Physical Sciences Research Council | en_US |
qmul.funder | Bayesian Artificial Intelligence for Decision Making under Uncertainty::Engineering and Physical Sciences Research Council | en_US |
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Except where otherwise noted, this item's license is described as This item is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.