Please use this identifier to cite or link to this item: https://doi.org/10.1109/IEEM.2011.6118205
Title: Bayesian calibration of stochastic computer models
Authors: Yuan, J.
Ng, S.H. 
Keywords: Bayesian calibration
EM algorithm
Gaussian process
stochastic computer models
Issue Date: 2011
Source: Yuan, J.,Ng, S.H. (2011). Bayesian calibration of stochastic computer models. IEEE International Conference on Industrial Engineering and Engineering Management : 1695-1699. ScholarBank@NUS Repository. https://doi.org/10.1109/IEEM.2011.6118205
Abstract: Computer models are widely used to simulate real processes. Within the computer model, there always exist some parameters which are unobservable in the real process but need to be specified in the computer model. The procedure to adjust these unknown parameters in order to fit the model to observed data and improve its predictive capability is known as calibration. In traditional calibration, once the optimal calibration parameter set is obtained, it is treated as known for future prediction. Calibration parameter uncertainty introduced from estimation is not accounted for. We will present a Bayesian calibration approach for stochastic computer models. We account for these additional uncertainties and derive the predictive distribution for the real process. Two numerical examples are used to illustrate the accuracy of the proposed method. © 2011 IEEE.
Source Title: IEEE International Conference on Industrial Engineering and Engineering Management
URI: http://scholarbank.nus.edu.sg/handle/10635/72293
ISBN: 9781457707391
ISSN: 21573611
DOI: 10.1109/IEEM.2011.6118205
Appears in Collections:Staff Publications

Show full item record
Files in This Item:
There are no files associated with this item.

Page view(s)

40
checked on Dec 9, 2017

Google ScholarTM

Check

Altmetric


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.