Assessing parameter uncertainty on coupled models using minimum information methods

Tim Bedford, Kevin Wilson, Alireza Daneshkhah

Research output: Contribution to journalArticlepeer-review

11 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


Dive into the research topics of 'Assessing parameter uncertainty on coupled models using minimum information methods'. Together they form a unique fingerprint.

Cite this