Conference article

Simulation Metamodeling using Dynamic Bayesian Networks with Multiple Time Scales

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

Download articlehttp://dx.doi.org/10.3384/ecp17142619

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

Linköping Electronic Conference Proceedings 142:90, p. 619-625

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Published: 2018-12-19

ISBN: 978-91-7685-399-3

ISSN: 1650-3686 (print), 1650-3740 (online)

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.

Keywords

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

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