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Assessing parameter uncertainty on coupled models using minimum information methods

Lookup NU author(s): Dr Kevin Wilson



This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC BY-NC-ND).


Probabilistic inversion is used to take expert uncertainty assessments about observable model outputs and build from them a distribution on the model parameters that captures the uncertainty expressed by the experts. In this paper we look at ways to use minimum information methods to do this, focussing in particular on the problem of ensuring consistency between expert assessments about differing variables, either as outputs from a single model or potentially as outputs along a chain of models. The paper shows how such a problem can be structured and then illustrates the method with two examples; one involving failure rates of equipment in series systems and the other atmospheric dispersion and deposition.

Publication metadata

Author(s): Bedford T, Wilson KJ, Daneshkhah A

Publication type: Article

Publication status: Published

Journal: Reliability Engineering and System Safety

Year: 2014

Volume: 125

Pages: 3-12

Print publication date: 01/05/2014

Online publication date: 24/05/2013

Acceptance date: 15/05/2013

Date deposited: 18/04/2016

ISSN (print): 0951-8320

ISSN (electronic): 1879-0836

Publisher: Elsevier


DOI: 10.1016/j.ress.2013.05.011


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