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Bayesian sequential data collection for stochastic simulation calibration
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DOI：10.1016/j.ejor.2019.01.073.png)
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Abstract
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Simulation is often used to guide the decision making for real complex stochastic systems. To faithfully assess the mean performance of the real system, it is necessary to efficiently calibrate the simulation model. Existing calibration approaches are typically built on the summary statistics of simulation outputs and ignore the serial dependence of detailed output sample paths. Given a tight simulation budget, we develop a Bayesian sequential data collection approach for simulation calibration via exploring the detailed simulation outputs. Then, the calibrated simulation model can be used to guide decision making. Both theoretical and empirical studies demonstrate that we can efficiently use the simulation resources and achieve better calibration accuracy by exploring the first two moment dynamic information of simulation output sample paths. (C) 2019 Elsevier B.V. All rights reserved.
Keywords:
Simulation
Calibration
Sequential experiment design
Detailed sample paths
Bayesian method
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