Assessing parameter uncertainty on coupled models using minimum information methods

Tim Bedford, Kevin Wilson, Alireza Daneshkhah

    Research output: Contribution to journalArticlepeer-review

    13 Citations (Scopus)


    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.
    Original languageEnglish
    Pages (from-to)3-12
    Number of pages10
    JournalReliability Engineering & System Safety
    Early online date24 May 2013
    Publication statusPublished - May 2014


    • Coupled models
    • Probabilistic Risk Analysis
    • Expert Judgement
    • minimum information


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