Uncertainty modelling on coupled models using minimum information methods

Tim Bedford, Kevin J. Wilson, Alireza Daneshkhah

Research output: Chapter in Book/Report/Conference proceedingConference proceeding

1 Citation (Scopus)

Abstract

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 an example involving atmospheric dispersion and deposition.

Original languageEnglish
Title of host publication11th International Probabilistic Safety Assessment and Management Conference and the Annual European Safety and Reliability Conference 2012, PSAM11 ESREL 2012
Pages6801-6810
Number of pages10
Volume8
Publication statusPublished - 1 Dec 2012
Externally publishedYes
Event11th International Probabilistic Safety Assessment and Management Conference and the Annual European Safety and Reliability Conference 2012, PSAM11 ESREL 2012 - Helsinki, Finland
Duration: 25 Jun 201229 Jun 2012

Conference

Conference11th International Probabilistic Safety Assessment and Management Conference and the Annual European Safety and Reliability Conference 2012, PSAM11 ESREL 2012
CountryFinland
CityHelsinki
Period25/06/1229/06/12

Keywords

  • Coupled models
  • Expert judgement
  • Minimum information
  • Probabilistic risk analysis

ASJC Scopus subject areas

  • Safety, Risk, Reliability and Quality

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