We use cookies to ensure that we give you the best experience on our website. By continuing to browse this repository, you give consent for essential cookies to be used. You can read more about our Privacy and Cookie Policy.

Durham Research Online
You are in:

Bayesian inference for reliability of systems and networks using the survival signature.

Aslett, L.J.M. and Coolen, F.P.A. and Wilson, S.P. (2015) 'Bayesian inference for reliability of systems and networks using the survival signature.', Risk analysis., 35 (9). pp. 1640-1651.


The concept of survival signature has recently been introduced as an alternative to the signature for reliability quantification of systems. While these two concepts are closely related for systems consisting of a single type of component, the survival signature is also suitable for systems with multiple types of component, which is not the case for the signature. This also enables the use of the survival signature for reliability of networks. In this article, we present the use of the survival signature for reliability quantification of systems and networks from a Bayesian perspective. We assume that data are available on tested components that are exchangeable with those in the actual system or network of interest. These data consist of failure times and possibly right-censoring times. We present both a nonparametric and parametric approach.

Item Type:Article
Keywords:Bayesian methods, Networks, Nonparametrics, Parametric lifetime distributions, System reliability.
Full text:(AM) Accepted Manuscript
Download PDF
Publisher Web site:
Publisher statement:This is the accepted version of the following article: Aslett, L. J. M., Coolen, F. P. A. and Wilson, S. P. (2015), Bayesian Inference for Reliability of Systems and Networks Using the Survival Signature. Risk Analysis, 35(9): 1640-1651., which has been published in final form at This article may be used for non-commercial purposes in accordance With Wiley Terms and Conditions for self-archiving.
Date accepted:01 June 2014
Date deposited:05 October 2015
Date of first online publication:11 June 2014
Date first made open access:11 June 2016

Save or Share this output

Look up in GoogleScholar