Thesis

Structured expert judgement for dependence in probabilistic modelling of uncertainty, advances along the dependence elicitation process

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Awarding institution
  • University of Strathclyde
Date of award
  • 2018
Thesis identifier
  • T14974
Person Identifier (Local)
  • 201382266
Qualification Level
Qualification Name
Department, School or Faculty
Abstract
  • In decision and risk analysis problems, modelling uncertainty probabilistically provides key insights and information for decision makers. A common challenge is that uncertainties are typically not isolated but interlinked which introduces complex (and often unexpected) effects on the model output. Therefore, dependence needs to be taken into account and modelled appropriately if simplifying assumptions, such as independence, are not sensible.;Similar to the case of univariate uncertainty, relevant historical data to quantify a (dependence) model are often lacking or too costly to obtain. This may be true even when data on a model's univariate quantities, such as marginal probabilities, are available. Then, specifying dependence between the uncertain variables through expert judgement is the only sensible option. A structured and formal process to the elicitation is essential for ensuring methodological robustness.;This thesis consists of three published works and two papers which are to be published (one under review and one working paper). Two of these works provide comprehensive overviews from different perspectives about the research on dependence elicitation processes. Based on these reviews, novel risk assessment and expert judgement methods are proposed - (1) allowing experts to structure and share their knowledge and beliefs about dependence relationships prior to a quantitative assessment and (2) ensuring experts' (detailed) quantitative assessments are feasible while their elicitation is intuitive.;The original research presented in this thesis is applied in case-studies with experts in real risk modelling contexts for the UK Higher Education sector, terrorism risk and future risk of antibacterial multi-drug resistance.
Advisor / supervisor
  • Bedford, T.
  • Quigley J.
Resource Type
DOI
Date Created
  • 2018
Former identifier
  • 9912631892902996

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