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dc.contributor.authorYet, Barbaros
dc.contributor.authorConstantinou, Anthony
dc.contributor.authorFenton, Norman
dc.contributor.authorNeil, Martin
dc.date.accessioned2020-01-17T13:54:06Z
dc.date.available2020-01-17T13:54:06Z
dc.date.issued2018
dc.identifier.issn2169-3536
dc.identifier.urihttps://doi.org/10.1109/ACCESS.2018.2799527
dc.identifier.urihttp://hdl.handle.net/11655/21809
dc.description.abstractIn decision theory models, expected value of partial perfect information (EVPPI) is an important analysis technique that is used to identify the value of acquiring further information on individual variables. EVPPI can be used to prioritize the parts of a model that should be improved or identify the parts where acquiring additional data or expert knowledge is most beneficial. Calculating EVPPI of continuous variables is challenging, and several sampling and approximation techniques have been proposed. This paper proposes a novel approach for calculating EVPPI in hybrid influence diagram (HID) models (these are influence diagrams (IDs) containing both discrete and continuous nodes). The proposed approach transforms the HID into a hybrid Bayesian network and makes use of the dynamicdiscretization and the junction tree algorithms to calculate the EVPPI. This is an approximate solution (no feasible exact solution is possible generally for HIDs) but we demonstrate it accurately calculates the EVPPI values. Moreover, unlike the previously proposed simulation-based EVPPI methods, our approach eliminates the requirement of manually determining the sample size and assessing convergence. Hence, it can be used by decision-makers who do not have deep understanding of programming languages and sampling techniques. We compare our approach to the previously proposed techniques based on two case studies.tr_TR
dc.language.isoentr_TR
dc.publisherIeee-Inst Electrical Electronics Engineers Inctr_TR
dc.relation.isversionof10.1109/ACCESS.2018.2799527tr_TR
dc.rightsinfo:eu-repo/semantics/openAccesstr_TR
dc.subjectcomputer sciencetr_TR
dc.subjectengineeringtr_TR
dc.subjecttelecommunicationstr_TR
dc.subject.lcshMühendisliktr_TR
dc.titleExpected Value Of Partial Perfect Information In Hybrid Models Using Dynamic Discretizationtr_en
dc.typeinfo:eu-repo/semantics/articletr_TR
dc.typeinfo:eu-repo/semantics/publishedVersion
dc.relation.journalIeee Accesstr_TR
dc.contributor.departmentEndüstri Mühendisliğitr_TR
dc.identifier.volume6tr_TR
dc.identifier.startpage7802tr_TR
dc.identifier.endpage7817tr_TR
dc.description.indexWoStr_TR
dc.description.indexScopustr_TR
dc.fundingYoktr_TR


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