A preposterior analysis to predict identifiability in the experimental calibration of computer models

Paul D. Arendt, Daniel W. Apley and Wei Chen

IISE Transactions, 2016, vol. 48, issue 1, 75-88

Abstract: When using physical experimental data to adjust, or calibrate, computer simulation models, two general sources of uncertainty that must be accounted for are calibration parameter uncertainty and model discrepancy. This is complicated by the well-known fact that systems to be calibrated are often subject to identifiability problems, in the sense that it is difficult to precisely estimate the parameters and to distinguish between the effects of parameter uncertainty and model discrepancy. We develop a form of preposterior analysis that can be used, prior to conducting physical experiments but after conducting the computer simulations, to predict the degree of identifiability that will result after conducting the physical experiments for a given experimental design. Specifically, we calculate the preposterior covariance matrix of the calibration parameters and demonstrate that, in the examples that we consider, it provides a reasonable prediction of the actual posterior covariance that is calculated after the experimental data are collected. Consequently, the preposterior covariance can be used as a criterion for designing physical experiments to help achieve better identifiability in calibration problems.

Date: 2016
References: Add references at CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://hdl.handle.net/10.1080/0740817X.2015.1064554 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:taf:uiiexx:v:48:y:2016:i:1:p:75-88

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/uiie20

DOI: 10.1080/0740817X.2015.1064554

Access Statistics for this article

IISE Transactions is currently edited by Jianjun Shi

More articles in IISE Transactions from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().