Please use this identifier to cite or link to this item: https://doi.org/10.1109/WSC.2013.6721453
Title: An entropy based sequential calibration approach for stochastic computer models
Authors: Jun, Y.
Ng, S.H. 
Issue Date: 2013
Citation: Jun, Y.,Ng, S.H. (2013). An entropy based sequential calibration approach for stochastic computer models. Proceedings of the 2013 Winter Simulation Conference - Simulation: Making Decisions in a Complex World, WSC 2013 : 589-600. ScholarBank@NUS Repository. https://doi.org/10.1109/WSC.2013.6721453
Abstract: Computer models are widely used to simulate complex and costly real processes and systems. In the calibration process of the computer model, the calibration parameters are adjusted to fit the model closely to the real observed data. As these calibration parameters are unknown and are estimated based on observed data, it is important to estimate it accurately and account for the estimation uncertainty in the subsequent use of the model. In this paper, we study in detail an empirical Bayes approach for stochastic computer model calibration that accounts for various uncertainties including the calibration parameter uncertainty, and propose an entropy based criterion to improve on the estimation of the calibration parameter. This criterion is also compared with the EIMSPE criterion. © 2013 IEEE.
Source Title: Proceedings of the 2013 Winter Simulation Conference - Simulation: Making Decisions in a Complex World, WSC 2013
URI: http://scholarbank.nus.edu.sg/handle/10635/87318
ISBN: 9781479939503
DOI: 10.1109/WSC.2013.6721453
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