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)
112 Downloads (Pure)


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 and System Safety
Issue numberspecial issue
Early online date24 May 2013
Publication statusPublished - 1 May 2014
EventPSAM11 & ESREL 2012 - Helsinki, Finland
Duration: 25 Jun 201229 Jun 2012


  • minimum information
  • coupled models
  • expert judgement
  • probabilistic risk analysis
  • gaussian plume
  • parameter uncertainty
  • coupled models
  • minimum information methods


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