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Bayesian Evidence Synthesis and the quantification of uncertainty in a Monte Carlo simulation

Author:
  • Ullrika Sahlin
  • Yf Jiang
Publishing year: 2016-10-01
Language: English
Pages: 445-456
Publication/Series: Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability
Volume: 230
Issue: 5
Document type: Journal article
Publisher: Proc. IMechE, PartO

Abstract english

Monte Carlo simulation is a useful technique to propagate uncertainty through a quantitative model, but that is all. When the quantitative modelling is used to support decision-making, a Monte Carlo simulation must be complemented by a conceptual framework that assigns a meaningful interpretation of uncertainty in output. Depending on how the assessor or decision maker choose to perceive risk, the interpretation of uncertainty and the way uncertainty ought to be treated and assigned to input variables in a Monte Carlo simulation will differ. Bayesian Evidence Synthesis is a framework for model calibration and quantitative modelling which has originated from complex meta-analysis in medical decision-making that conceptually can frame a Monte Carlo simulation. We ask under what perspectives on risk that Bayesian Evidence Synthesis is a suitable framework. The discussion is illustrated by Bayesian Evidence Synthesis applied on a population viability analysis used in ecological risk assessment and a reliability analysis of a repairable system informed by multiple sources of evidence. We conclude that Bayesian Evidence Synthesis can conceptually frame a Monte Carlo simulation under a Bayesian perspective on risk. It can also frame an assessment under a general perspective of risk since Bayesian Evidence Synthesis provide principles of predictive inference that constitute an unbroken link between evidence and assessment output that open up for uncertainty quantified taking qualitative aspects of knowledge into account.

Keywords

  • Probability Theory and Statistics
  • Bayesian calibration
  • Epistemic uncertainty
  • meta-analysis
  • quantitative assessment
  • risk concept

Other

Published
  • ISSN: 1748-006X
me in Lundagård
E-mail: ullrika.sahlin [at] cec.lu.se

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Centre for Environmental and Climate Research (CEC)

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