Simulation Metamodeling Using Dynamic Bayesian Networks with Multiple Time Scales

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dc.contributor Aalto-yliopisto fi
dc.contributor Aalto University en
dc.contributor.author Harju, Mikko
dc.contributor.author Virtanen, Kai
dc.contributor.author Poropudas, Jirka
dc.contributor.editor Juuso, Esko
dc.contributor.editor Dahlquist, Erik
dc.contributor.editor Leiviskä, Kauko
dc.date.accessioned 2019-02-25T08:54:25Z
dc.date.available 2019-02-25T08:54:25Z
dc.date.issued 2018-12-19
dc.identifier.citation Harju , M , Virtanen , K & Poropudas , J 2018 , Simulation Metamodeling Using Dynamic Bayesian Networks with Multiple Time Scales . in E Juuso , E Dahlquist & K Leiviskä (eds) , Proceedings of The 9th EUROSIM Congress on Modelling and Simulation (EUROSIM 2016), The 57th SIMS Conference on Simulation and Modelling (SIMS 2016) . , 090 , Linköping electronic conference proceedings , no. 142 , LINKÖPING UNIVERSITY ELECTRONIC PRESS , pp. 619-625 , EUROSIM Congress on Modelling and Simulation & SIMS Conference on Simulation and Modelling , Oulu , Finland , 12/09/2016 . https://doi.org/10.3384/ecp17142619 en
dc.identifier.isbn 978-91-7685-399-3
dc.identifier.issn 1650-3686
dc.identifier.issn 1650-3740
dc.identifier.other PURE UUID: da178a64-2a63-469a-8a0a-54e36aaa9249
dc.identifier.other PURE ITEMURL: https://research.aalto.fi/en/publications/simulation-metamodeling-using-dynamic-bayesian-networks-with-multiple-time-scales(da178a64-2a63-469a-8a0a-54e36aaa9249).html
dc.identifier.other PURE FILEURL: https://research.aalto.fi/files/31662465/ecp17142090.pdf
dc.identifier.uri https://aaltodoc.aalto.fi/handle/123456789/36910
dc.description.abstract The utilization of dynamic Bayesian networks (DBNs) in simulation metamodeling enables the investigation of the time evolution of state variables of a simulation model. DBN metamodels have previously described the changes in the probability distribution of the simulation state by using a time slice structure in which the state variables are described at common time instants. In this paper, the novel approach to the determination of the time slice structure is introduced. It enables the selection of time instants of the DBN separately for each state variable. In this way, a more accurate metamodel representing multiple time scales of the variables is achieved. Furthermore, the construction is streamlined by presenting a dynamic programming algorithm for determining the key time instants for individual variables. The construction and use of the DBN metamodels are illustrated by an example problem dealing with the simulated operation of an air base. en
dc.format.extent 7
dc.format.extent 619-625
dc.format.mimetype application/pdf
dc.language.iso en en
dc.publisher Linköping University Electronic Press
dc.relation.ispartof EUROSIM Congress on Modelling and Simulation & SIMS Conference on Simulation and Modelling en
dc.relation.ispartofseries Proceedings of The 9th EUROSIM Congress on Modelling and Simulation (EUROSIM 2016), The 57th SIMS Conference on Simulation and Modelling (SIMS 2016) en
dc.relation.ispartofseries Linköping electronic conference proceedings en
dc.relation.ispartofseries issue 142 en
dc.rights openAccess en
dc.subject.other 111 Mathematics en
dc.title Simulation Metamodeling Using Dynamic Bayesian Networks with Multiple Time Scales en
dc.type A4 Artikkeli konferenssijulkaisussa fi
dc.description.version Peer reviewed en
dc.contributor.department Department of Mathematics and Systems Analysis
dc.contributor.department Department of Mathematics and Systems Analysis en
dc.subject.keyword Bayesian networks
dc.subject.keyword discrete event simulation
dc.subject.keyword dynamic Bayesian networks
dc.subject.keyword simulation
dc.subject.keyword simulation metamodeling
dc.subject.keyword 111 Mathematics
dc.identifier.urn URN:NBN:fi:aalto-201902252067
dc.identifier.doi 10.3384/ecp17142619
dc.type.version publishedVersion


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