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dc.contributor.authorBull, Lawrence
dc.contributor.authorDhada, Maharshi
dc.contributor.authorSteinert, Olof
dc.contributor.authorLindgren, Tony
dc.contributor.authorParlikad, Ajith Kumar
dc.contributor.authorGirolami, Mark
dc.date.accessioned2022-03-28T23:30:36Z
dc.date.available2022-03-28T23:30:36Z
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/335452
dc.description.abstractPopulation-level modelling is used to address issues of data sparsity in the survival analysis of a simulated truck fleet. Specifically, hierarchical Bayes with mixed effects improves the predictive capability of hazard models. A set of correlated functions is learnt over the vehicle population – in a combined model – to approximate fleet predictors. Model uncertainty is reduced when sub-fleets of vehicles are allowed to share correlated information. In turn, vehicle groups with incomplete data (automatically) borrow statistical strength from data-rich groups.
dc.description.sponsorshipWave 1 of The UKRI Strategic Priorities Fund under the EPSRC Grant EP/W006022/1, particularly the Ecosystems of Digital Twins theme within that grant and The Alan Turing Institute. Next Generation Converged Digital Infrastructure project (EP/R004935/1) funded by the Engineering and Physical Sciences Research Council and BT.
dc.rightsAll Rights Reserved
dc.rights.urihttp://www.rioxx.net/licenses/all-rights-reserved
dc.titlePopulation-Level Modelling for Truck Fleet Survival Analysis
dc.typeConference Object
dc.publisher.departmentDepartment of Engineering
dc.date.updated2022-03-26T12:34:38Z
dc.identifier.doi10.17863/CAM.82881
rioxxterms.versionofrecord10.17863/CAM.82881
rioxxterms.versionAM
dc.contributor.orcidParlikad, Ajith [0000-0001-6214-1739]
pubs.conference-nameEuropean Workshop on Structural Health Monitoring
cam.orpheus.counter24*
cam.depositDate2022-03-26
pubs.licence-identifierapollo-deposit-licence-2-1
pubs.licence-display-nameApollo Repository Deposit Licence Agreement
rioxxterms.freetoread.startdate2023-03-28


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