An experimental procedure for simulation response surface model identification
Communications of the ACM
Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
A model for reasoning about persistence and causation
Computational Intelligence
The simulation metamodel
Proceedings of the 30th conference on Winter simulation
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Bayesian Artificial Intelligence
Bayesian Artificial Intelligence
Optimization by simulation metamodelling methods
WSC '04 Proceedings of the 36th conference on Winter simulation
Simulation Modeling and Analysis (McGraw-Hill Series in Industrial Engineering and Management)
Simulation Modeling and Analysis (McGraw-Hill Series in Industrial Engineering and Management)
Analyzing air combat simulation results with dynamic Bayesian networks
Proceedings of the 39th conference on Winter simulation: 40 years! The best is yet to come
Bayesian Networks and Decision Graphs
Bayesian Networks and Decision Graphs
Game-theoretic validation and analysis of air combat simulation models
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans - Special issue on model-based diagnostics
Influence diagrams in analysis of discrete event simulation data
Winter Simulation Conference
Continuous time bayesian networks
UAI'02 Proceedings of the Eighteenth conference on Uncertainty in artificial intelligence
Learning continuous time bayesian networks
UAI'03 Proceedings of the Nineteenth conference on Uncertainty in Artificial Intelligence
Multiple input and multiple output simulation metamodeling using Bayesian networks
Proceedings of the Winter Simulation Conference
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The application of dynamic Bayesian networks (DBNs) is a recently introduced approach to simulation metamodeling where the probability distribution of simulation state is represented as a function of time. The DBN metamodels reveal the time evolution of simulation and enable alternative what-if analyses unlike previous metamodels that imitate the simulation model as an input-output mapping. In earlier studies, the analysis of DBNs is restricted to discrete time instants selected beforehand in the construction phase of the metamodel. This paper introduces an extension to the framework of DBN metamodeling that employs multivariate interpolation and allows the analysis in continuous time. In practice, an approximation for the probability distribution of the simulation state is calculated by interpolating between conditional probabilities given by the DBN. The utilization of multivariate interpolation in the context of DBN metamodeling is illustrated by examples dealing with Poisson arrival processes and air combat simulation.