Article | Proceedings of The 9th EUROSIM Congress on Modelling and Simulation, EUROSIM 2016, The 57th SIMS Conference on Simulation and Modelling SIMS 2016 | Simulation Metamodeling using Dynamic Bayesian Networks with Multiple Time Scales Linköping University Electronic Press Conference Proceedings
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Title:
Simulation Metamodeling using Dynamic Bayesian Networks with Multiple Time Scales
Author:
Mikko Harju: Department of Mathematics and Systems Analysis, Aalto University School of Science, Finland Kai Virtanen: Department of Mathematics and Systems Analysis, Aalto University School of Science, Finland Jirka Poropudas: Department of Mathematics and Systems Analysis, Aalto University School of Science, Finland
DOI:
10.3384/ecp17142619
Download:
Full text (pdf)
Year:
2018
Conference:
Proceedings of The 9th EUROSIM Congress on Modelling and Simulation, EUROSIM 2016, The 57th SIMS Conference on Simulation and Modelling SIMS 2016
Issue:
142
Article no.:
090
Pages:
619-625
No. of pages:
7
Publication type:
Abstract and Fulltext
Published:
2018-12-19
ISBN:
978-91-7685-399-3
Series:
Linköping Electronic Conference Proceedings
ISSN (print):
1650-3686
ISSN (online):
1650-3740
Publisher:
Linköping University Electronic Press, Linköpings universitet


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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.

Keywords: Bayesian networks, discrete event simulation, dynamic Bayesian networks, simulation, simulation metamodeling

Proceedings of The 9th EUROSIM Congress on Modelling and Simulation, EUROSIM 2016, The 57th SIMS Conference on Simulation and Modelling SIMS 2016

Author:
Mikko Harju, Kai Virtanen, Jirka Poropudas
Title:
Simulation Metamodeling using Dynamic Bayesian Networks with Multiple Time Scales
DOI:
http://dx.doi.org/10.3384/ecp17142619
References:
No references available

Proceedings of The 9th EUROSIM Congress on Modelling and Simulation, EUROSIM 2016, The 57th SIMS Conference on Simulation and Modelling SIMS 2016

Author:
Mikko Harju, Kai Virtanen, Jirka Poropudas
Title:
Simulation Metamodeling using Dynamic Bayesian Networks with Multiple Time Scales
DOI:
https://doi.org10.3384/ecp17142619
Note: the following are taken directly from CrossRef
Citations:
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Last updated: 2019-10-02