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dc.contributor.authorShenvi, A
dc.contributor.authorSmith, JQ
dc.contributor.authorWalton, R
dc.contributor.authorEldridge, S
dc.date.accessioned2020-12-02T15:20:52Z
dc.date.available2020-12-02T15:20:52Z
dc.date.issued2019-01-01
dc.identifier.citationShenvi A., Smith J.Q., Walton R., Eldridge S. (2019) Modelling with Non-stratified Chain Event Graphs. In: Argiento R., Durante D., Wade S. (eds) Bayesian Statistics and New Generations. BAYSM 2018. Springer Proceedings in Mathematics & Statistics, vol 296. Springer, Cham. https://doi.org/10.1007/978-3-030-30611-3_16en_US
dc.identifier.isbn9783030306106
dc.identifier.issn2194-1009
dc.identifier.urihttps://qmro.qmul.ac.uk/xmlui/handle/123456789/68926
dc.description.abstract© 2019, Springer Nature Switzerland AG. Chain Event Graphs (CEGs) are recent probabilistic graphical modelling tools that have proved successful in modelling scenarios with context-specific independencies. Although the theory underlying CEGs supports appropriate representation of structural zeroes, the literature so far does not provide an adaptation of the vanilla CEG methods for a real-world application presenting structural zeroes also known as the non-stratified CEG class. To illustrate these methods, we present a non-stratified CEG representing a public health intervention designed to reduce the risk and rate of falling in the elderly. We then compare the CEG model to the more conventional Bayesian Network model when applied to this setting.en_US
dc.format.extent155 - 163
dc.relation.ispartofseriesSpringer Proceedings in Mathematics & Statistics;
dc.titleModelling with non-stratified chain event graphsen_US
dc.typeConference Proceedingen_US
dc.identifier.doi10.1007/978-3-030-30611-3_16
pubs.notesNot knownen_US
pubs.publication-statusPublisheden_US
pubs.volume296en_US
rioxxterms.funderDefault funderen_US
rioxxterms.identifier.projectDefault projecten_US


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