Simulation Metamodeling Using Dynamic Bayesian Networks with Multiple Time Scales

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review


Research units


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.


Original languageEnglish
Title of host publicationProceedings of The 9th EUROSIM Congress on Modelling and Simulation (EUROSIM 2016), The 57th SIMS Conference on Simulation and Modelling (SIMS 2016)
EditorsEsko Juuso, Erik Dahlquist, Kauko Leiviskä
Publication statusPublished - 19 Dec 2018
MoE publication typeA4 Article in a conference publication
EventEUROSIM Congress on Modelling and Simulation & SIMS Conference on Simulation and Modelling - Oulu, Finland
Duration: 12 Sep 201616 Sep 2016

Publication series

NameLinköping electronic conference proceedings
PublisherLinköping University Electronic Press
ISSN (Print)1650-3686
ISSN (Electronic)1650-3740


ConferenceEUROSIM Congress on Modelling and Simulation & SIMS Conference on Simulation and Modelling
Abbreviated titleEUROSIM 2016 and SIMS 2016

    Research areas

  • Bayesian networks, discrete event simulation, dynamic Bayesian networks, simulation, simulation metamodeling

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