Multiple input and multiple output simulation metamodeling using Bayesian networks

This paper proposes a novel approach tomultiple input andmultiple output (MIMO) simulationmetamodeling using Bayesian networks (BNs). ABNis a probabilisticmodel that represents the joint probability distribution of a set of randomvariables and enables the efficient calculation of theirmarginal and c...

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Bibliographic Details
Main Authors: Poropudas, J., Pousi, J., Virtanen, K.
Format: Conference Proceeding
Language:English
Subjects:
Analytical models
Bayesian methods
Data models
Joints
Probability distribution
Random variables
Uncertainty
Online Access:Request full text
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Summary:This paper proposes a novel approach tomultiple input andmultiple output (MIMO) simulationmetamodeling using Bayesian networks (BNs). ABNis a probabilisticmodel that represents the joint probability distribution of a set of randomvariables and enables the efficient calculation of theirmarginal and conditional distributions. A BN metamodel gives a non-parametric description for the joint probability distribution of randomvariables representing simulation inputs and outputs by combining MIMO data provided by stochastic simulation with available background knowledge about the system under consideration. The BN metamodel allows various what-if analyses that are used for studying the marginal probability distributions of the outputs, the input uncertainty, the dependence between the inputs and the outputs, and the dependence between the outputs as well as for inverse reasoning. The construction and utilization of BN metamodels in simulation studies are illustrated with an example involving a queueing model.
ISSN:0891-7736
1558-4305
DOI:10.1109/WSC.2011.6147786