Bayesian Evidence Synthesis and the quantification of uncertainty in a Monte Carlo simulation
Sahlin, Ullrika LU
and Jiang, Yf
(2016)
In Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability
230(5).
p.445-456
- Abstract
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... (More)
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.
(Less)
- author
- Sahlin, Ullrika
LU
and Jiang, Yf
- organization
- publishing date
- 2016-10-01
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- Bayesian calibration, Epistemic uncertainty, meta-analysis, quantitative assessment, risk concept
- in
- Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability
- volume
- 230
- issue
- 5
- pages
- 12 pages
- publisher
- Proc. IMechE, PartO
- external identifiers
-
- scopus:84990841492
- wos:000386032800002
- ISSN
- 1748-006X
- DOI
- 10.1177/1748006X15609003
- project
- Uncertainty and Evidence Lab
- language
- English
- LU publication?
- yes
- id
- db01651b-26af-45b5-92fc-10b2131bfc5c
- date added to LUP
- 2016-11-16 11:13:53
- date last changed
- 2024-06-28 19:07:59
@article{db01651b-26af-45b5-92fc-10b2131bfc5c,
abstract = {{<p>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.</p>}},
author = {{Sahlin, Ullrika and Jiang, Yf}},
issn = {{1748-006X}},
keywords = {{Bayesian calibration; Epistemic uncertainty; meta-analysis; quantitative assessment; risk concept}},
language = {{eng}},
month = {{10}},
number = {{5}},
pages = {{445--456}},
publisher = {{Proc. IMechE, PartO}},
series = {{Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability}},
title = {{Bayesian Evidence Synthesis and the quantification of uncertainty in a Monte Carlo simulation}},
url = {{http://dx.doi.org/10.1177/1748006X15609003}},
doi = {{10.1177/1748006X15609003}},
volume = {{230}},
year = {{2016}},
}