Model validation based on random set theory
by Liang Zhao
International Journal of Reliability and Safety (IJRS), Vol. 11, No. 1/2, 2017

Abstract: This paper considers the model validation under epistemic uncertainty in model inputs. The random set theory is used to quantify the uncertainty of model prediction. With the probability box obtained by the random set theory, a pignistic probability transformation is applied to construct a single probability distribution to be the prior distribution for the model prediction. Then a posterior probability distribution is updated based on the experimental observations in Bayesian principle. The Bayes factor derived from the ratio between the posterior and the prior probability distributions is used as the validation metric to quantify the extent to which the experimental observations support the model. A thermal conduction example and an aerospace bolted joint example are presented to illustrate the proposed method. It is shown that the method presented in this paper provides a convenient mechanism to consider different types of uncertainty during the model validation.

Online publication date: Mon, 11-Dec-2017

The full text of this article is only available to individual subscribers or to users at subscribing institutions.

 
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.

Pay per view:
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.

Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Reliability and Safety (IJRS):
Login with your Inderscience username and password:

Username:        Password:          Forgotten your password?

Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.

If you still need assistance, please email subs@inderscience.com